IAIFI Astrophysics Papers

Astrophysics

On Soft Clustering For Correlation Estimators: Model Uncertainty, Differentiability, and Surrogates
Edward Berman, Sneh Pandya, Jacqueline McCleary, Marko Shuntov, Caitlin Casey, Nicole Drakos, Andreas Faisst, Steven Gillman, Ghassem Gozaliasl, Natalie Hogg, Jeyhan Kartaltepe, Anton Koekemoer, Wilfried Mercier, Diana Scognamiglio, COSMOS-Web: The JWST Cosmic Origins Survey
[ arXiv:2504.06174 | code ]

Abstract Properly estimating correlations between objects at different spatial scales necessitates (n2) distance calculations. For this reason, most widely adopted packages for estimating correlations use clustering algorithms to approximate local trends. However, methods for quantifying the error introduced by this clustering have been understudied. In response, we present an algorithm for estimating correlations that is probabilistic in the way that it clusters objects, enabling us to quantify the uncertainty caused by clustering simply through model inference. These soft clustering assignments enable correlation estimators that are theoretically differentiable with respect to their input catalogs. Thus, we also build a theoretical framework for differentiable correlation functions and describe their utility in comparison to existing surrogate models. Notably, we find that repeated normalization and distance function calls slow gradient calculations and that sparse Jacobians destabilize precision, pointing towards either approximate or surrogate methods as a necessary solution to exact gradients from correlation functions. To that end, we close with a discussion of surrogate models as proxies for correlation functions. We provide an example that demonstrates the efficacy of surrogate models to enable gradient-based optimization of astrophysical model parameters, successfully minimizing a correlation function output. Our numerical experiments cover science cases across cosmology, from point spread function (PSF) modeling efforts to gravitational simulations to galaxy intrinsic alignment (IA).

The Type I Superluminous Supernova Catalogue II: Spectroscopic Evolution in the Photospheric Phase, Velocity Measurements, and Constraints on Diversity
Aysha Aamer, Matt Nicholl, Sebastian Gomez, Edo Berger, Peter Blanchard, Joseph P. Anderson, Charlotte Angus, Amar Aryan, Chris Ashall, Ting-Wan Chen, Georgios Dimitriadis, Lluis Galbany, Anamaria Gkini, Mariusz Gromadzki, Claudia P. Gutierrez, Daichi Hiramatsu, Griffin Hosseinzadeh, Cosimo Inserra, Amit Kumar, Hanindyo Kuncarayakti, Giorgos Leloudas, Paolo Mazzali, Kyle Medler, Tomás E. Müller-Bravo, Mauricio Ramirez, Aiswarya Sankar.K, Steve Schulze, Avinash Singh, Jesper Sollerman, Shubham Srivastav, Jacco H. Terwel, David R. Young
[ arXiv:2503.21874 ]

Abstract Hydrogen-poor superluminous supernovae (SLSNe) are among the most energetic explosions in the universe, reaching luminosities up to 100 times greater than those of normal supernovae. Detailed spectral analysis hold the potential to reveal their progenitors and underlying energy sources. This paper presents the largest compilation of SLSN photospheric spectra to date, encompassing data from ePESSTO+, the FLEET search and all published spectra up to December 2022. The dataset includes a total of 974 spectra of 234 SLSNe. By constructing average phase binned spectra, we find SLSNe initially exhibit high temperatures (10000 to 11000 K), with blue continua and weak lines. A rapid transformation follows, as temperatures drop to 5000 to 6000 K by 40 days post peak, leading to stronger P-Cygni features. These averages also suggest a fraction of SLSNe may contain some He at explosion. Variance within the dataset is slightly reduced when defining the phase of spectra relative to explosion, rather than peak, and normalising to the population's median e-folding time. Principal Component Analysis (PCA) supports this, requiring fewer components to explain the same level of variation when binning data by scaled days from explosion, suggesting a more homogeneous grouping. Using PCA and K-Means clustering, we identify outlying objects with unusual spectroscopic evolution and evidence for energy input from interaction, but find not support for groupings of two or more statistically significant subpopulations. We find Fe II {lambda}5169 lines velocities closely track the radius implied from blackbody fits, indicating formation near the photosphere. We also confirm a correlation between velocity and velocity gradient, which can be explained if all SLSNe are in homologous expansion but with different scale velocities. This behaviour aligns with expectations for an internal powering mechanism.

Decoding the Galactic Twirl: The Downfall of Milky Way-mass Galaxies Rotation Curves in the FIRE Simulations
Xiaowei Ou, Lina Necib, Andrew Wetzel, Anna Frebel, Jeremy Bailin, Micah Oeur
[ arXiv:2503.05877 ]

Abstract Recent measurements of the Milky Way rotation curve found a sharp decline at around 15-20 kpc from the center of the Galaxy, suggesting that the Galactic dark matter halo is much less massive than predicted by other dynamical tracers. To address this tension, we study the validity of the assumptions made in calculating the Milky Way's rotation curve. To do so, we apply Jeans' equation, the current standard approach of measuring rotation curves, to three cosmological zoom-in simulations of Milky Way-like galaxies from the FIRE-2 Latte suite. Using synthetic Gaia surveys, we replicate the sample selection process and calculation employed in measuring the Milky Way rotation curve. We examine four failure modes of this calculation and find that the measured curves deviate from the true curve by 5-20% rather than below 5%, as estimated by previous works. Interestingly, there is a large galaxy-to-galaxy variance, and different systematics dominate different galaxies. We rederive the Milky Way's dark matter density profile with the rotation curve while incorporating systematics from the simulations. The posterior distribution of the density profiles is consistent with a fiducial NFW profile when assuming a gNFW profile for dark matter. We find that the virial mass, 7.32+1.98−1.53×1011 M⊙, consistent with other probes of the Milky Way's mass. However, we recommend that the field moves away from relying solely on the rotation curve when studying the dark matter profile, and adopts methods that incorporate additional probes and/or do not heavily depend on assumptions described in this study.

Trial by FIRE: Probing the dark matter density profile of dwarf galaxies with GraphNPE
Tri Nguyen, Justin Read, Lina Necib, Siddharth Mishra-Sharma, Claude-André Faucher-Giguère, Andrew Wetzel
[ arXiv:2503.03812 ]

Abstract The Dark Matter (DM) distribution in dwarf galaxies provides crucial insights into both structure formation and the particle nature of DM. GraphNPE (Graph Neural Posterior Estimator), first introduced in Nguyen et al. (2023), is a novel simulation-based inference framework that combines graph neural networks and normalizing flows to infer the DM density profile from line-of-sight stellar velocities. Here, we apply GraphNPE to satellite dwarf galaxies in the FIRE-2 Latte simulation suite of Milky Way-mass halos, testing it against both Cold and Self-Interacting DM scenarios. Our method demonstrates superior precision compared to conventional Jeans-based approaches, recovering DM density profiles to within the 95% confidence level even in systems with as few as 30 tracers. Moreover, we present the first evaluation of mass modeling methods in constraining two key parameters from realistic simulations: the peak circular velocity, Vmax, and the peak virial mass, Mpeak200m. Using only line-of-sight velocities, GraphNPE can reliably recover both Vmax and Mpeak200m within our quoted uncertainties, including those experiencing tidal effects (≳ 63% of systems are recovered with our 68% confidence intervals and ≳ 92% within our 95% confidence intervals). The method achieves 10-20% accuracy in Vmax recovery, while Mpeak200m is recovered to 0.1-0.4 dex accuracy. This work establishes GraphNPE as a robust tool for inferring DM density profiles in dwarf galaxies, offering promising avenues for constraining DM models. The framework's potential extends beyond this study, as it can be adapted to non-spherical and disequilibrium models, showcasing the broader utility of simulation-based inference and graph-based learning in astrophysics.

Deriving Stellar Properties, Distances, and Reddenings using Photometry and Astrometry with BRUTUS
Joshua S. Speagle, Catherine Zucker, Angus Beane, Phillip A. Cargile, Aaron Dotter, Douglas P. Finkbeiner, Gregory M. Green, Benjamin D. Johnson, Edward F. Schlafly, Ana Bonaca, Charlie Conroy, Gwendolyn Eadie, Daniel J. Eisenstein, Alyssa A. Goodman, Jiwon Jesse Han, Harshil M. Kamdar, Rohan Naidu, Hans-Walter Rix, Andrew K. Saydjari, Yuan-Sen Ting, Ioana A. Zelko
[ arXiv:2503.02227 | code ]

Abstract We present brutus, an open source Python package for quickly deriving stellar properties, distances, and reddenings to stars based on grids of stellar models constrained by photometric and astrometric data. We outline the statistical framework for deriving these quantities, its implementation, and various Galactic priors over the 3-D distribution of stars, stellar properties, and dust extinction (including RV variation). We establish a procedure to empirically calibrate MIST v1.2 isochrones by using open clusters to derive corrections to the effective temperatures and radii of the isochrones, which reduces systematic errors on the lower main sequence. We also describe and apply a method to estimate photometric offsets between stellar models and observed data using nearby, low-reddening field stars. We perform a series of tests on mock and real data to examine parameter recovery with MIST under different modeling assumptions, illustrating that brutus is able to recover distances and other stellar properties using optical to near-infrared photometry and astrometry.

A Deep, High-Angular Resolution 3D Dust Map of the Southern Galactic Plane
Catherine Zucker, Andrew K. Saydjari, Joshua S. Speagle, Edward F. Schlafly, Gregory M. Green, Robert Benjamin, Joshua Peek, Gordian Edenhofer, Alyssa Goodman, Michael A. Kuhn, Douglas P. Finkbeiner
[ arXiv:2503.02657 ]

Abstract We present a deep, high-angular resolution 3D dust map of the southern Galactic plane over 239∘<ℓ<6∘ and |b|<10∘ built on photometry from the DECaPS2 survey, in combination with photometry from VVV, 2MASS, and unWISE and parallaxes from Gaia DR3 where available. To construct the map, we first infer the distance, extinction, and stellar types of over 700 million stars using the brutus stellar inference framework with a set of theoretical MIST stellar models. Our resultant 3D dust map has an angular resolution of 1′, roughly an order of magnitude finer than existing 3D dust maps and comparable to the angular resolution of the Herschel 2D dust emission maps. We detect complexes at the range of distances associated with the Sagittarius-Carina and Scutum-Centaurus arms in the fourth quadrant, as well as more distant structures out to a maximum reliable distance of d≈ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughly AV≈12 mag. We publicly release both the stellar catalog and the 3D dust map, the latter of which can easily be queried via the Python package dustmaps. When combined with the existing Bayestar19 3D dust map of the northern sky, the DECaPS 3D dust map fills in the missing piece of the Galactic plane, enabling extinction corrections over the entire disk |b|<10∘. Our map serves as a pathfinder for the future of 3D dust mapping in the era of LSST and Roman, targeting regimes accessible with deep optical and near-infrared photometry but often inaccessible with Gaia.

Evidence for an Instability-Induced Binary Merger in the Double-Peaked, Helium-Rich Type IIn Supernova 2023zkd
A. Gagliano, V. A. Villar, T. Matsumoto, D. O. Jones, C. L. Ransome, A. E. Nugent, D. Hiramatsu, K. Auchettl, D. Tsuna, Y. Dong, S. Gomez, P. D. Aleo, C. Angus, T. de Boer, K. A. Bostroem, K. C. Chambers, D. A. Coulter, K. W. Davis, J. R. Fairlamb, J. Farah, D. Farias, R. J. Foley, C. Gall, H. Gao, E. P. Gonzalez, D. A. Howell, M. E. Huber, C. D. Kilpatrick, C.-C. Lin, M. E. MacLeod, E. A. Magnier, C. McCully, P. Minguez, G. Narayan, M. Newsome, K. C. Patra, A. Rest, S. Rest, S. Smartt, K. W. Smith, G. Terreran, R. J. Wainscoat, Q. Wang, S. K. Yadavalli, Y. Zenati (The Young Supernova Experiment)
[ arXiv:2502.19469 ]

Abstract We present ultraviolet to infrared observations of the extraordinary Type IIn supernova 2023zkd (SN 2023zkd). Photometrically, it exhibits persistent and luminous precursor emission spanning ∼4 years preceding discovery (Mr≈−15 mag, 1,500~days in the observer frame), followed by a secondary stage of gradual brightening in its final year. Post-discovery, it exhibits two photometric peaks of comparable brightness (Mr≲−18.7 mag and Mr≈−18.4 mag, respectively) separated by 240 days. Spectroscopically, SN 2023zkd exhibits highly asymmetric and multi-component Balmer and He I profiles that we attribute to ejecta interaction with fast-moving (1,000−2,000kms−1) He-rich polar material and slow-moving (∼400kms−1) equatorially-distributed H-rich material. He II features also appear during the second light curve peak and evolve rapidly. Shock-driven models fit to the multi-band photometry suggest that the event is powered by interaction with ∼5−6M⊙ of CSM, with 2−3M⊙ associated with each light curve peak, expelled during mass-loss episodes ∼3−4 and ∼1−2 years prior to explosion. The observed precursor emission, combined with the extreme mass-loss rates required to power each light curve peak, favors either super-Eddington accretion onto a black hole or multiple long-lived eruptions from a massive star to luminosities that have not been previously observed. We consider multiple progenitor scenarios for SN 2023zkd, and find that the brightening optical precursor and inferred explosion properties are most consistent with a massive (MZAMS≥30M⊙) and partially-stripped He star undergoing an instability-induced merger with a black hole companion.

Seeing the Outer Edge of the Infant Type Ia Supernova 2024epr in the Optical and Near Infrared
W. B. Hoogendam, D. O. Jones, C. Ashall, B. J. Shappee, R. J. Foley, M. A. Tucker, M. E. Huber, K. Auchettl, D. D. Desai, A. Do, J. T. Hinkle, S. Romagnoli, J. Shi, A. Syncatto, C. R. Angus, K. C. Chambers, D. A. Coulter, K. W. Davis, T. de Boer, A. Gagliano, M. Kong, C.-C. Lin, T. B. Lowe, E. A. Magnier, P. Minguez, Y.-C. Pan, K.C. Patra, S. A. Severson, K. Taggart, A. R. Wasserman, S. K. Yadavalli
[ arXiv:2502.17556 ]

Abstract We present optical-to-near infrared (NIR) photometry and spectroscopy of the Type Ia supernova (SN Ia) 2024epr, including NIR spectra observed within two days of first light. The early-time optical spectra show strong, high-velocity Ca and Si features near rarely-observed velocities at ∼0.1c, and the NIR spectra show a CI 'knee.' Despite these high-velocity features at early times, SN~2024epr evolves into a normal SN Ia, albeit with stronger peak-light Ca absorption than other SNe Ia with the same light curve shape. Although we infer a normal decline rate, Δm15(B)=1.09±0.12 mag, from the light-curve rise, SN 2024epr is a Branch 'cool' object and has red early-time colors (g−r≈0.15 mag at −10 days). The high velocities point to a density enhancement in the outer layers of the explosion, but thick-shell He-detonation models do not match the smoothly rising light curve or lack of He in our early-time NIR spectra. No current models (e.g., delayed detonation or thin He shell double detonation) appear to reproduce all of the observed properties. Such constraints are only possible for SN 2024epr from the earliest optical and NIR observations, highlighting their importance for constraining SN Ia models. Finally, we find several other SNe Ia with intermediate mass elements at ∼30,000 km s−1 within days after the explosion that evolve into otherwise normal SNe Ia at peak light, suggesting the early-time spectra of SNe Ia may hide a broad diversity of observational characteristics.

A Poisson Process AutoDecoder for X-ray Sources
Yanke Song, Victoria Ashley Villar, Juan Rafael Martinez-Galarza, Steven Dillmann
[ arXiv:2502.01627 ]

Abstract X-ray observing facilities, such as the Chandra X-ray Observatory and the eROSITA, have detected millions of astronomical sources associated with high-energy phenomena. The arrival of photons as a function of time follows a Poisson process and can vary by orders-of-magnitude, presenting obstacles for common tasks such as source classification, physical property derivation, and anomaly detection. Previous work has either failed to directly capture the Poisson nature of the data or only focuses on Poisson rate function reconstruction. In this work, we present Poisson Process AutoDecoder (PPAD). PPAD is a neural field decoder that maps fixed-length latent features to continuous Poisson rate functions across energy band and time via unsupervised learning. PPAD reconstructs the rate function and yields a representation at the same time. We demonstrate the efficacy of PPAD via reconstruction, regression, classification and anomaly detection experiments using the Chandra Source Catalog.

A Fast Periodicity Detection Algorithm Sensitive to Arbitrary Waveforms
Douglas P. Finkbeiner, Thomas A. Prince, Samuel E. Whitebook
[ arXiv:2502.00243 ]

Abstract A reexamination of period finding algorithms is prompted by new large area astronomical sky surveys that can identify billions of individual sources having a thousand or more observations per source. This large increase in data necessitates fast and efficient period detection algorithms. In this paper, we provide an initial description of an algorithm that is being used for detection of periodic behavior in a sample of 1.5 billion objects using light curves generated from Zwicky Transient Facility (ZTF) data (Bellm et al. 2019; Masci et al. 2018). We call this algorithm 'Fast Periodicity Weighting' (FPW), derived using a Gaussian Process (GP) formalism. A major advantage of the FPW algorithm for ZTF analysis is that it is agnostic to the details of the phase-folded waveform. Periodic sources in ZTF show a wide variety of waveforms, some quite complex, including eclipsing objects, sinusoidally varying objects also exhibiting eclipses, objects with cyclotron emission at various phases, and accreting objects with complex waveforms. We describe the FPW algorithm and its application to ZTF, and provide efficient code for both CPU and GPU.

Central densities of dark matter halos in FIRE-2 simulations of low-mass galaxies with cold dark matter and self-interacting dark matter
Maria C. Straight, Michael Boylan-Kolchin, James S. Bullock, Philip F. Hopkins, Xuejian Shen, Lina Necib, Alexandres Lazar, Andrew S. Graus, Jenna Samuel
[ arXiv:2501.16602 ]

Abstract We investigate the central density structure of dark matter halos in cold dark matter (CDM) and self-interacting dark matter (SIDM) models using simulations that are part of the Feedback In Realistic Environments (FIRE) project. For simulated halos of dwarf galaxy scale (Mhalo(z=0)≈1010M⊙), we study the central structure in both dissipationless simulations and simulations with full FIRE-2 galaxy formation physics. As has been demonstrated extensively in recent years, both baryonic feedback and self-interactions can convert central cusps into cores, with the former process doing so in a manner that depends sensitively on stellar mass at fixed Mhalo. Whether the two processes (baryonic feedback and self-interactions) are distinguishable, however, remains an open question. Here we demonstrate that, compared to feedback-induced cores, SIDM-induced cores transition more quickly from the central region of constant density to the falling density at larger radial scales. This result holds true even when including identical galaxy formation modeling in SIDM simulations as is used in CDM simulations, since self-interactions dominate over galaxy formation physics in establishing the central structure of SIDM halos in this mass regime. The change in density profile slope as a function of radius therefore holds the potential to discriminate between self-interactions and galaxy formation physics as the driver of core formation in dwarf galaxies.

A theoretical approach to density-split clustering
Mathilde Pinon, Arnaud de Mattia, Étienne Burtin, Vanina Ruhlmann-Kleider, Sandrine Codis, Enrique Paillas, Carolina Cuesta-Lazaro
[ arXiv:2501.14638 | code ]

Abstract We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlation between density-split regions and the tracer density field. We show that these correlation functions can be expressed in terms of the two-point probability density function (PDF) of the density field, or equivalently, its bias function. We derive analytical predictions using three levels of approximation for the two-point PDF: a bivariate Gaussian distribution, a bivariate shifted log-normal distribution, and a prediction based on the Large Deviation Theory framework. Under spherical symmetry – where spherical collapse dynamics apply, such as for count-in-cell densities with spherical top-hat smoothing – LDT predicts the density two-point PDF in the large-separation regime relative to the smoothing radius. We validate our model against dark matter N-body simulations in real space, incorporating Poisson shot noise. Our results show that the LDT predictions outperform the log-normal approximation, and agrees with simulations on large scales within the cosmic variance of a typical DESI DR1 sample, despite relying on only one degree of freedom.

Theoretical Predictions for the Inner Dark Matter Distribution in the Milky Way Informed by Simulations
Abdelaziz Hussein, Lina Necib, Manoj Kaplinghat, Stacy Y. Kim, Andrew Wetzel, Justin I. Read, Martin P. Rey, Oscar Agertz
[ arXiv:2501.14868 | code ]

Abstract We build a theoretical range for the Milky Way's (MW) inner dark matter (DM) distribution informed by the FIRE-2, Auriga, VINTERGATAN-GM, and TNG50 simulation suites assuming the canonical cold dark matter (CDM) model. The DM density profiles in Auriga, VINTERGATAN-GM, and TNG50 can be approximately modeled using the adiabatic contraction prescription of Gnedin et al. 2004, while FIRE-2 has stronger baryonic feedback, leading to a departure from the adiabatic contraction model. The simulated halos that are adiabatically contracted are close to spherical (axis ratio q∈[0.75−0.9] at 5∘), whereas halos that experience strong baryonic feedback are oblate (q∈[0.5−0.7]). Using the adiabatic contraction and strong baryonic feedback models, along with the observed stellar distribution of the MW, the inner logarithmic density slope for CDM in the MW is predicted to range from −0.5 to −1.3. The J-factor, which determines the DM-annihilation flux, averaged over a solid angle of 5∘ (10∘) is predicted to span the range 0.8-30 (0.6-10) ×1023GeV2/cm5. The D-factor, which determines the flux due to DM decay, is predicted to be in the range 0.6-2 (0.5−1) ×1023GeV/cm2.

An Updated Detection Pipeline for Precursor Emission in Type II Supernova 2020tlf
Wynn Jacobson-Galán, Sebastian Gonzalez, Shreyas Patel, Luc Dessart, David Jones, Deanne Coppejans, Georgios Dimitriadis, Ryan J. Foley, Charles D. Kilpatrick, David Matthews, Sofia Rest, Giacomo Terreran, Patrick D. Aleo, Katie Auchettl, Peter K. Blanchard, David A. Coulter, Kyle W. Davis, Thomas de Boer, Lindsay DeMarchi, Maria R. Drout, Nicholas Earl, Alexander Gagliano, Christa Gall, Jens Hjorth, Mark E. Huber, Adaeze L. Ibik, Danny Milisavljevic, Yen-Chen Pan, Armin Rest, Ryan Ridden-Harper, Cesar Rojas-Bravo, Matthew R. Siebert, Ken W. Smith, Kirsty Taggart, Samaporn Tinyanont, Qinan Wang, Yossef Zenati
The American Astronomical Society, Volume 9, Number 1, 2025 [ arXiv:2501.08475 ]

Abstract We present a new photometric pipeline for the detection of pre-supernova (pre-SN) emission in the Young Supernova Experiment (YSE) sky survey. The method described is applied to SN 2020tlf, a type II SN (SN II) with precursor emission in the last ~100 days before first light. We re-analyze the YSE griz-band light curves of SN 2020tlf and provide revised pre-explosion photometry that includes a robust list of confident detection and limiting magnitudes. Compared to the results of Jacobson-Galan et al. 2022a, this new analysis yields fewer total r/i/z-band pre-SN detections at phases > -100 days. Furthermore, we discourage the use of the blackbody modeling of the pre-explosion spectral energy distribution, the pre-SN bolometric light curve and the blackbody model parameters presented in Jacobson-Galan et al. 2022a. Nevertheless, binned photometry of SN 2020tlf confirms a consistent progenitor luminosity of ~1040 erg s−1 before explosion.

Effects of galactic environment on size and dark matter content in low-mass galaxies
Francisco J. Mercado, Jorge Moreno, Robert Feldmann, Marckie Zeender, Jose A. Benavides, Joanna M. Piotrowska, Courtney Klein, Coral Wheeler, Lina Necib, James S. Bullock, Philip F. Hopkins
[ arXiv:2501.04084 ]

Abstract We utilize the cosmological volume simulation, FIREbox, to investigate how a galaxy's environment influences its size and dark matter content. Our study focuses on approximately 1,200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between 106 to 109 M⊙. We analyze the size-mass relation (r50−M⋆), inner dark matter mass-stellar mass (M50DM−M⋆) relation, and the halo mass-stellar mass (Mhalo−M⋆) relation. At fixed stellar mass, we find the galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI > 1) are generally larger and have lower masses relative to their counterparts with lower Perturbation Indices (PI < 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass (Mrmhalo) are significant predictors of a galaxy's relative size and dark matter content. Notably, because Mhalo is also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution.

Exoplanet Detection via Differentiable Rendering
Brandon Y. Feng, Rodrigo Ferrer-Chávez, Aviad Levis, Jason J. Wang, Katherine L. Bouman, William T. Freeman
IEEE Transactions on Computational Imaging, 2025, Volume 11 [ arXiv:2501.01912 ]

Abstract Direct imaging of exoplanets is crucial for advancing our understanding of planetary systems beyond our solar system, but it faces significant challenges due to the high contrast between host stars and their planets. Wavefront aberrations introduce speckles in the telescope science images, which are patterns of diffracted starlight that can mimic the appearance of planets, complicating the detection of faint exoplanet signals. Traditional post-processing methods, operating primarily in the image intensity domain, do not integrate wavefront sensing data. These data, measured mainly for adaptive optics corrections, have been overlooked as a potential resource for post-processing, partly due to the challenge of the evolving nature of wavefront aberrations. In this paper, we present a differentiable rendering approach that leverages these wavefront sensing data to improve exoplanet detection. Our differentiable renderer models wave-based light propagation through a coronagraphic telescope system, allowing gradient-based optimization to significantly improve starlight subtraction and increase sensitivity to faint exoplanets. Simulation experiments based on the James Webb Space Telescope configuration demonstrate the effectiveness of our approach, achieving substantial improvements in contrast and planet detection limits. Our results showcase how the computational advancements enabled by differentiable rendering can revitalize previously underexploited wavefront data, opening new avenues for enhancing exoplanet imaging and characterization.

Cosmological constraints from the Minkowski functionals of the BOSS CMASS galaxy sample
Wei Liu, Enrique Paillas, Carolina Cuesta-Lazaro, Georgios Valogiannis, Wenjuan Fang
[ arXiv:2501.01698 ]

Abstract For the first time, we develop a simulation-based model for the Minkowski functionals (MFs) of large-scale structure, which allows us to extract the full information available from the MFs (including both the Gaussian and non-Gaussian part), and apply it to the BOSS DR12 CMASS galaxy sample. Our model is based on high-fidelity mock galaxy catalogs constructed from the extsc{Abacus} extsc{Summit} simulations using the halo occupation distribution (HOD) framework, which include the redshift-space distortions and Alcock-Paczynski distortions, incorporate survey realism, including survey geometry and veto masks, and account for angular plus radial selection effects. The cosmological and HOD parameter dependence of the MFs is captured with a neural network emulator trained from the galaxy mocks with various cosmological and HOD parameters. To benchmark the constraining power of the MFs, we also train an emulator for the galaxy 2-point correlation function (2PCF) using the same pipeline. Having validated our approach through successful parameter recovery tests on both internal and external mocks, including non-HOD forward models of the halo-galaxy connection, we apply our forward model to analyze the CMASS data in the redshift range 0.45<z<0.58. We find the MFs provide stronger constraints on the cosmological parameters than the 2PCF. The combination of the two gives ωcdm=0.1172+0.0020−0.0023, σ8=0.783±0.026, and ns=0.966+0.019−0.015, which are tighter by a factor of 2.0, 1.9, and 1.6 than the 2PCF alone. The derived constraint fσ8=0.453±0.016 is also improved by a factor of 1.9, compared to the 2PCF, and agrees well with Planck 2018 predictions and other results from a series of studies in the literature.

ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST
Ved G. Shah, Alex Gagliano, Konstantin Malanchev, Gautham Narayan, The LSST Dark Energy Science Collaboration
[ arXiv:2501.01496 | code ]

Abstract We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with Gated Recurrent Units (GRUs), and has been trained using a custom hierarchical cross-entropy loss function to provide high-confidence classifications along an observationally-driven taxonomy with as little as a single photometric observation. Contextual information for each object, including host galaxy photometric redshift, offset, ellipticity and brightness, is concatenated to the light curve embedding and used to make a final prediction. Training on ∼0.5M events from the Extended LSST Astronomical Time-Series Classification Challenge, we achieve a top-level (Transient vs Variable) macro-averaged precision of 0.96 using only 1 day of photometric observations after the first detection in addition to contextual information, for each event; this increases to >0.99 once 64 days of the light curve has been obtained, and 0.83 at 1024 days after first detection for 19-way classification (including supernova sub-types, active galactic nuclei, variable stars, microlensing events, and kilonovae). We also compare ORACLE with other state-of-the-art classifiers and report comparable performance for the 19-way classification task, in addition to delivering accurate top-level classifications much earlier.

A Near-IR Search for Helium in the Superluminous Supernova SN 2024ahr
Harsh Kumar, Edo Berger, Peter K. Blanchard, Sebastian Gomez, Daichi Hiramatsu, Moira Andrews, K. Azalee Bostroem, Yize Dong, Joseph Farah, Estefania Padilla Gonzalez, D. Andrew Howell, Curtis McCully, Darshana Mehta, Megan Newsome, Aravind P. Ravi, Giacomo Terreran
[ arXiv:2501.01485 ]

Abstract We present a detailed study of SN 2024ahr, a hydrogen-poor superluminous supernova (SLSN-I), for which we determine a redshift of z=0.0861. SN 2024ahr has a peak absolute magnitude of Mg≈Mr≈−21 mag, rest-frame rise and decline times (50% of peak) of about 40 and 80 days, respectively, and typical spectroscopic evolution in the optical band. Similarly, modeling of the UV/optical light curves with a magnetar spin-down engine leads to typical parameters: an initial spin period of ≈3.3 ms, a magnetic field strength of ≈6×1013 G, and an ejecta mass of ≈9.5 M⊙. Due to its relatively low redshift we obtained a high signal-to-noise ratio near-IR spectrum about 43 rest-frame days post-peak to search for the presence of helium. We do not detect any significant feature at the location of the He I λ2.058 μm feature, and place a conservative upper limit of ∼0.05 M⊙ on the mass of helium in the outer ejecta. We detect broad features of Mg I λ1.575 μm and a blend of Co II λ2.126 μm and Mg II, λ2.136 μm, which are typical of Type Ic SNe, but with higher velocities. Examining the sample of SLSNe-I with NIR spectroscopy, we find that, unlike SN 2024ahr, these events are generally peculiar. This highlights the need for a large sample of prototypical SLSNe-I with NIR spectroscopy to constrain the fraction of progenitors with helium (Ib-like) and without helium (Ic-like) at the time of the explosion, and hence the evolutionary path(s) leading to the rare outcome of SLSNe-I.

The z≳9 galaxy UV luminosity function from the JWST Advanced Deep Extragalactic Survey: insights into early galaxy evolution and reionization
Lily Whitler, Daniel P. Stark, Michael W. Topping, Brant Robertson, Marcia Rieke, Kevin N. Hainline, Ryan Endsley, Zuyi Chen, William M. Baker, Rachana Bhatawdekar, Andrew J. Bunker, Stefano Carniani, Stéphane Charlot, Jacopo Chevallard, Emma Curtis-Lake, Eiichi Egami, Daniel J. Eisenstein, Jakob M. Helton, Zhiyuan Ji, Benjamin D. Johnson, Pablo G. Pérez-González, Pierluigi Rinaldi, Sandro Tacchella, Christina C. Williams, Christopher N. A. Willmer, Chris Willott, Joris Witstok
[ arXiv:2501.00984 ]

Abstract The high-redshift UV luminosity function provides important insights into the evolution of early galaxies. JWST has revealed an unexpectedly large population of bright (MUV≲−20) galaxies at z≳10, implying fundamental changes in the star forming properties of galaxies at increasingly early times. However, constraining the fainter population (MUV≳−18) has been more challenging. In this work, we present the z≳9 UV luminosity function from the JWST Advanced Deep Extragalactic Survey. We calculate the UV luminosity function from several hundred z≳9 galaxy candidates that reach UV luminosities of MUV∼−17 in redshift bins of z∼9−12 (309 candidates) and z∼12−16 (63 candidates). We search for candidates at z∼16−22.5 and find none. We also estimate the z∼14−16 luminosity function from the z≥14 subset of the z∼12−16 sample. Consistent with other measurements, we find an excess of bright galaxies that is in tension with many theoretical models, especially at z≳12. However, we also find high number densities at −18≲MUV≲−17, suggesting that there is a larger population of faint galaxies than expected, as well as bright ones. From our parametric fits for the luminosity function, we find steep faint end slopes of −2.5≲α≲−2.3, suggesting a large population of faint (MUV≳−17) galaxies. Combined, the high normalization and steep faint end slope of the luminosity function could imply that the reionization process is appreciably underway as early as z=10.

The Impact of Host-galaxy Properties on Supernova Classification with Hierarchical Labels
Villar, V. Ashley, Gomes, Sebastian, Berger, Edo, Gagliano,Alex
The Astrophysical Journal Supplement Series, Voulume 276, Number 1, 2024 [ ]

Abstract With the advent of the Vera C. Rubin Observatory, the discovery rate of supernovae (SNe) will surpass the rate of SNe with real time spectroscopic follow-up by 3 orders of magnitude. Accurate photometric classifiers are essential to both select interesting events for follow-up in real time and for archival population-level studies. In this work, we investigate the impact of observable host-galaxy information on the classification of SNe, both with and without additional light-curve and redshift information. We find that host-galaxy information alone can successfully isolate relatively pure (>90%) samples of Type Ia SNe with or without redshift information. With redshift information, we can additionally produce somewhat pure (>70%) samples of Type II SNe and superluminous SNe. Additionally with redshift information, host-galaxy properties do not significantly improve the accuracy of SN classification when paired with complete light curves. In the absence of redshift information, however, galaxy properties significantly increase the accuracy of photometric classification. As a part of this analysis, we present the first formal application of a new objective function, the weighted hierarchical cross entropy, to the problem of SN classification. This objective function more naturally accounts for the hierarchical nature of SN classes and, more broadly, transients. Finally, we present a new set of SN classifications for the Pan-STARRS Medium Deep Survey of SNe that lack spectroscopic redshift, increasing the full photometric sample to >4400 events.

The Millennium and Astrid galaxies in effective field theory: comparison with galaxy-halo connection models at the field level
Mikhail M. Ivanov, Carolina Cuesta-Lazaro, Andrej Obuljen, Michael W. Toomey, Yueying Ni, Sownak Bose, Boryana Hadzhiyska, César Hernández-Aguayo, Lars Hernquist, Rahul Kannan, Volker Springel
[ arXiv:2412.01888 ]

Abstract Cosmological analyses of redshift space clustering data are primarily based on using luminous ``red'' galaxies (LRGs) and ``blue'' emission line galaxies (ELGs) to trace underlying dark matter. Using the large high-fidelity high-resolution MillenniumTNG (MTNG) and Astrid simulations, we study these galaxies with the effective field theory (EFT)-based field level forward model. We confirm that both red and blue galaxies can be accurately modeled with EFT at the field level and their parameters match those of the phenomenological halo-based models. Specifically, we consider the state of the art Halo Occupation Distribution (HOD) and High Mass Quenched (HMQ) models for the red and blue galaxies, respectively. Our results explicitly confirm the validity of the halo-based models on large scales beyond the two-point statistics. In addition, we validate the field-level HOD/HMQ-based priors for EFT full-shape analysis. We find that the local bias parameters of the ELGs are in tension with the predictions of the LRG-like HOD models and present a simple analytic argument explaining this phenomenology. We also confirm that ELGs exhibit weaker non-linear redshift-space distortions (``fingers-of-God''), suggesting that a significant fraction of their data should be perturbative. We find that the response of EFT parameters to galaxy selection is sensitive to assumptions about baryonic feedback, suggesting that a detailed understanding of feedback processes is necessary for robust predictions of EFT parameters. Finally, using neural density estimation based on paired HOD-EFT parameter samples, we obtain optimal HOD models that reproduce the clustering of Astrid and MTNG galaxies.

Probing primordial non-Gaussianity by reconstructing the initial conditions
Xinyi Chen, Nikhil Padmanabhan, Daniel J. Eisenstein
[ arXiv:2412.00968 ]

Abstract We propose to constrain the primordial (local-type) non-Gaussianity signal by first reconstructing the initial density field to remove the late time non-Gaussianities introduced by gravitational evolution. Our reconstruction algorithm combines perturbation theory on large scales with a convolutional neural network on small scales. We reconstruct the squared potential (that sources the non-Gaussian signal) out to k=0.2 h/Mpc to an accuracy of 99.8%. We cross-correlate this squared potential field with the reconstructed density field and verify that this computationally inexpensive estimator has the same information content as the full matter bispectrum. As a proof of concept, our approach can yield up to a factor of three improvement in the fNL constraints, although it does not yet include the complications of galaxy bias or imperfections in the reconstruction. These potential improvements make it a promising alternative to current approaches to constraining primordial non-Gaussianity.

Type IIn Supernovae. I. Uniform Light Curve Characterization and a Bimodality in the Radiated Energy Distribution
Daichi Hiramatsu, Edo Berger, Sebastian Gomez, Peter K. Blanchard, Harsh Kumar, Wasundara Athukoralalage
[ arXiv:2411.07287 ]

Abstract We present the largest uniform study to date of Type IIn supernovae (SNe IIn), focusing in this first paper on the multi-band optical light curves of 487 SNe IIn. The sample, constructed from multiple surveys, extends to z≈0.8, with the majority of events at z≲0.3. We construct uniform multi-band and bolometric light curves using Gaussian process regression, and determine key observed properties in the rest-frame (e.g., peak luminosity, timescales, radiated energy). We find that SNe IIn span broad ranges in peak luminosity (∼1042−1044 erg s−1) and timescales (∼20−300 days above 50% of peak luminosity), but the sample divides into two clear groups in the luminosity-timescale phase-space around the median peak luminosity (≈1043 erg s−1): faint-fast and luminous-slow groups. This leads to a strong bimodality in the radiated energy distribution, with peaks at ∼1049 and ∼2×1050 erg, with the latter events having a characteristic timescale of ∼100 days, and the former appearing to bifurcate into two branches with timescales of ∼40 and ∼70 days. Therefore, SNe IIn exhibit at least two dominant groupings, and perhaps three, which are likely reflective of different progenitor and/or circumstellar medium formation pathways. We do not find any obvious transition in SN IIn properties at the arbitrary cut-off of ≈−20 mag used for the designation 'Type II Superluminous Supernovae', and we argue that this classification should be abandoned. The absence of SNe IIn with timescales of ≲14 days defines the region occupied by fast transients with evidence for interaction with hydrogen-poor circumstellar medium.

Conversations and Deliberations: Non-Standard Cosmological Epochs and Expansion Histories
Brian Batell, Keith R. Dienes, Brooks Thomas, Scott Watson, Rouzbeh Allahverdi, Mustafa Amin, Kimberly K. Boddy, M. Sten Delos, Adrienne L. Erickcek, Akshay Ghalsasi, John T. Giblin Jr., James Halverson, Fei Huang, Andrew J. Long, Lauren Pearce, Barmak Shams Es Haghi, Jessie Shelton, Gary Shiu, Kuver Sinha, Tristan L. Smith
[ arXiv:2411.04780 ]

Abstract This document summarizes the discussions which took place during the PITT-PACC Workshop entitled 'Non-Standard Cosmological Epochs and Expansion Histories,' held in Pittsburgh, Pennsylvania, Sept. 5-7, 2024. Much like the non-standard cosmological epochs that were the subject of these discussions, the format of this workshop was also non-standard. Rather than consisting of a series of talks from participants, with each person presenting their own work, this workshop was instead organized around free-form discussion blocks, with each centered on a different overall theme and guided by a different set of Discussion Leaders. This document is not intended to serve as a comprehensive review of these topics, but rather as an informal record of the discussions that took place during the workshop, in the hope that the content and free-flowing spirit of these discussions may inspire new ideas and research directions.

Inferring the Morphology of the Galactic Center Excess with Gaussian Processes
Edward D. Ramirez, Yitian Sun, Matthew R. Buckley, Siddharth Mishra-Sharma, Tracy R. Slatyer
Physical Review D, 2025, Vol. 111, Iss. 6 [ arXiv:2410.21367 | code ]

Abstract Descriptions of the Galactic Center using Fermi gamma-ray data have so far modeled the Galactic Center Excess (GCE) as a template with fixed spatial morphology or as a linear combination of such templates. Although these templates are informed by various physical expectations, the morphology of the excess is a priori unknown. For the first time, we describe the GCE using a flexible, non-parametric machine learning model -- the Gaussian process (GP). We assess our model's performance on synthetic data, demonstrating that the model can recover the templates used to generate the data. We then fit the \Fermi data with our model in a single energy bin from 2-20 GeV (leaving a spectral GP analysis of the GCE for future work) using a variety of template models of diffuse gamma-ray emission to quantify our fits' systematic uncertainties associated with diffuse emission modeling. We interpret our best-fit GP in terms of GCE templates consisting of an NFW squared template and a bulge component to determine which bulge models can best describe the fitted GP and to what extent the best-fit GP is described better by an NFW squared template versus a bulge template. The best-fit GP contains morphological features that are typically not associated with traditional GCE studies. These include a localized bright source at around (ℓ,b)=(20∘,0∘) and a diagonal arm extending Northwest from the Galactic Center. In spite of these novel features, the fitted GP is explained best by a template-based model consisting of the bulge presented in Coleman et al. (2020) and a squared NFW component. Our results suggest that the physical interpretation of the GCE in terms of stellar bulge and NFW-like components is highly sensitive to the assumed morphologies, background models, and the region of the sky used for inference.

Joint Modeling of Quasar Variability and Accretion Disk Reprocessing using Latent Stochastic Differential Equation
Joshua Fagin, James Hung-Hsu Chan, Henry Best, Matthew O’Dowd, K. E. Saavik Ford, Matthew J. Graham, Ji Won Park, V. Ashley Villar
[ arXiv:2410.18423 | code ]

Abstract Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing to fit simulated LSST 10-year quasar light curves. The driving variability is reconstructed using a latent stochastic differential equation, a physically motivated, generative deep learning method that can model continuous-time stochastic dynamics. By embedding these physical processes into our network, we achieve a model that is more robust and interpretable. We also use transformers to scale our model to tens of millions of parameters. We demonstrate how our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wavebands, even for out-of-distribution driving signals. Our approach provides a powerful and scalable framework that can be adapted to solve other inverse problems in multivariate time series with irregular sampling.

Blast: a Web Application for Characterizing the Host Galaxies of Astrophysical Transients
D. O. Jones, P. McGill, T. A. Manning, A. Gagliano, B. Wang, D. A. Coulter, R. J. Foley, G. Narayan, V. A. Villar, L. Braff, A. W. Engel, D. Farias, Z. Lai, K. Loertscher, J. Kutcka, S. Thorp, J. Vazquez
[ arXiv:2410.17322 ]

Abstract Characterizing the host galaxies of astrophysical transients is important to many areas of astrophysics, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients without photometric or spectroscopic data. Given the increasing transient discovery rate in the coming years, there is substantial utility in providing public, transparent, reproducible, and automatic characterization for large samples of transient host galaxies. Here we present Blast, a web application that ingests live streams of transient alerts, matches transients to their host galaxies, and performs photometry on coincident archival imaging data of the host galaxy. The photometry is then used to infer both global host-galaxy properties and galaxy properties within 2 kpc of the transient location by using the Prospector Bayesian inference framework, with an acceleration in evaluation speed achieved via simulation-based inference. Blast provides host-galaxy properties to users via a web browser or an application program interface. The software can be extended to support alternative photometric or SED-fitting algorithms, and can be scaled via an asynchronous worker queue across multiple compute nodes to handle the processing of large volumes of transient alerts for upcoming transient surveys. Blast has been ingesting newly discovered transients from the Transient Name Server since mid-2024, and has currently measured SED parameters for more than 6000 transients. The service is publicly available at this https URL: https://blast.scimma.org/.

Auriga Streams II: orbital properties of tidally disrupting satellites of Milky Way-mass galaxies
Nora Shipp, Alexander H. Riley, Christine M. Simpson, Rebekka Bieri, Lina Necib, Arpit Arora, Francesca Fragkoudi, Facundo A. Gómez, Robert J. J. Grand, Federico Marinacci
[ arXiv:2410.09143 ]

Abstract Galaxies like the Milky Way are surrounded by complex populations of satellites at all stages of tidal disruption. In this paper, we present a dynamical study of the disrupting satellite galaxies in the Auriga simulations that are orbiting 28 distinct Milky Way-mass hosts across three resolutions. We find that the satellite galaxy populations are highly disrupted. The majority of satellites that remain fully intact at present day were accreted recently without experiencing more than one pericentre (nperi≲1) and have large apocentres (rapo≳200 kpc) and pericentres (rperi≳50 kpc). The remaining satellites have experienced significant tidal disruption and, given full knowledge of the system, would be classified as stellar streams. We find stellar streams in Auriga across the range of pericentres and apocentres of the known Milky Way dwarf galaxy streams and, interestingly, overlapping significantly with the Milky Way intact satellite population. We find no significant change in satellite orbital distributions across resolution. However, we do see substantial halo-to-halo variance of (rperi,rapo) distributions across host galaxies, as well as a dependence of satellite orbits on host halo mass - systems disrupt at larger pericentres and apocentres in more massive hosts. Our results suggest that either cosmological simulations (including, but not limited to, Auriga) are disrupting satellites far too readily, or that the Milky Way's satellites are more disrupted than current imaging surveys have revealed. Future observing facilities and careful mock observations of these systems will be key to revealing the nature of this apparent discrepancy.

StreamGen: Connecting Populations of Streams and Shells to Their Host Galaxies
Adriana Dropulic, Nora Shipp, Stacy Kim, Zeineb Mezghanni, Lina Necib, Mariangela Lisanti
[ arXiv:2409.13810 | code ]

Abstract In this work, we study how the abundance and dynamics of populations of disrupting satellite galaxies change systematically as a function of host galaxy properties. We apply a theoretical model of the phase-mixing process to classify intact satellite galaxies, stellar stream-like and shell-like debris in ~1500 Milky Way-mass systems generated by a semi-analytic galaxy formation code, SatGen. In particular, we test the effect of host galaxy halo mass, disk mass, ratio of disk scale height to length, and stellar feedback model on disrupting satellite populations. We find that the counts of tidal debris are consistent across all host galaxy models, within a given host mass range, and that all models can have stream-like debris on low-energy orbits, consistent with those observed around the Milky Way. However, we find a preference for stream-like debris on lower-energy orbits in models with a thicker (lower-density) host disk or on higher-energy orbits in models with a more-massive host disk. Importantly, we observe significant halo-to-halo variance across all models. These results highlight the importance of simulating and observing large samples of Milky Way-mass galaxies and accounting for variations in host properties when using disrupting satellites in studies of near-field cosmology.

Full-shape analysis with simulation-based priors: cosmological parameters and the structure growth anomaly
Mikhail M. Ivanov, Andrej Obuljen, Carolina Cuesta-Lazaro, Michael W. Toomey
Physical Review D, 2025, Vol. 111, Iss. 6 [ arXiv:2409.10609 ]

Abstract We explore full-shape analysis with simulation-based priors, which is the simplest approach to galaxy clustering data analysis that combines effective field theory (EFT) on large scales and numerical simulations on small scales. The core ingredient of our approach is the prior density of EFT parameters which we extract from a suite of 10500 galaxy simulations based on the halo occupation distribution (HOD) model. We measure the EFT parameters with the field-level forward model, which enables us to cancel cosmic variance. On the theory side, we develop a new efficient approach to calculate field-level transfer functions using time-sliced perturbation theory and the logarithmic fast Fourier transform. We study cosmology dependence of EFT parameters of dark matter halos and HOD galaxies and find that it can be ignored for the purpose of prior generation. We use neural density estimation to model the measured distribution of EFT parameters. Our distribution model is then used as a prior in a reanalysis of the BOSS full-shape galaxy power spectrum data. Assuming the ΛCDM model, we find significant (≈30% and ≈60%) improvements for the matter density fraction and the mass fluctuation amplitude, which are constrained to Ωm=0.315±0.010 and σ8=0.671±0.027. The value of the Hubble constant does not change, H0=68.7±1.1 km/s/Mpc. This reaffirms earlier reports of the structure growth tension from the BOSS data. Finally, we use the measured EFT parameters to constrain galaxy formation physics.

How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds
Tri Nguyen, Francisco Villaescusa-Navarro, Siddharth Mishra-Sharma, Carolina Cuesta-Lazaro, Paul Torrey, Arya Farahi, Alex M. Garcia, Jonah C. Rose, Stephanie O’Neil, Mark Vogelsberger, Xuejian Shen, Cian Roche, Daniel Anglés-Alcázar, Nitya Kallivayalil, Julian B. Muñoz, Francis-Yan Cyr-Racine, Sandip Roy, Lina Necib, Kassidy E. Kollmann
[ arXiv:2409.02980 | code ]

Abstract The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this complex relationship. Many techniques have been developed to model this connection, from Halo Occupation Distribution (HOD) to empirical and semi-analytic models to hydrodynamic. Hydrodynamic simulations can incorporate more detailed astrophysical processes but are computationally expensive; HODs, on the other hand, are computationally cheap but have limited accuracy. In this work, we present NeHOD, a generative framework based on variational diffusion model and Transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD. By modeling galaxies/subhalos as point clouds, instead of binning or voxelization, we can resolve small spatial scales down to the resolution of the simulations. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We train NeHOD on the TNG-Warm DM suite of the DREAMS project, which consists of 1024 high-resolution zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM mass and astrophysical parameters. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters, including the mass functions, stellar-halo mass relations, concentration-mass relations, and spatial clustering. Our method can be used for a large variety of downstream applications, from galaxy clustering to strong lensing studies.

Maven: A Multimodal Foundation Model for Supernova Science
Gemma Zhang, Thomas Helfer, Alexander T. Gagliano, Siddharth Mishra-Sharma, V. Ashley Villar
Machine Learning Science and Tehnology, Volume , Number 4, 2024 [ arXiv:2408.16829 | code ]

Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5M synthetic supernovae using a constrastive objective. We then fine-tune the model on 4,702 observed supernovae from the Zwicky Transient Facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin Observatory, Maven serves as a Rosetta Stone for leveraging large, unlabeled and multimodal time-domain datasets.

Cosmological Parameter Estimation with a Joint-Likelihood Analysis of the Cosmic Microwave Background and Big Bang Nucleosynthesis
Cara Giovanetti, Mariangela Lisanti, Hongwan Liu, Siddharth Mishra-Sharma, Joshua T. Ruderman
[ arXiv:2408.14531 ]

Abstract We present the first joint-likelihood analysis of Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) data. Bayesian inference is performed on the baryon abundance and the effective number of neutrino species, Neff, using a CMB Boltzmann solver in combination with LINX, a new flexible and efficient BBN code. We marginalize over Planck nuisance parameters and nuclear rates to find Neff=3.08+0.13−0.13,2.94+0.16−0.16, or 2.98+0.14−0.13, for three separate reaction networks. This framework enables robust testing of the Lambda Cold Dark Matter paradigm and its variants with CMB and BBN data.

LINX: A Fast, Differentiable, and Extensible Big Bang Nucleosynthesis Package
Cara Giovanetti, Mariangela Lisanti, Hongwan Liu, Siddharth Mishra-Sharma, Joshua T. Ruderman
[ arXiv:2408.14538 | code ]

Abstract We introduce LINX (Light Isotope Nucleosynthesis with JAX), a new differentiable public Big Bang Nucleosynthesis (BBN) code designed for fast parameter estimation. By leveraging JAX, LINX achieves both speed and differentiability, enabling the use of Bayesian inference, including gradient-based methods. We discuss the formalism used in LINX for rapid primordial elemental abundance predictions and give examples of how LINX can be used. When combined with differentiable Cosmic Microwave Background (CMB) power spectrum emulators, LINX can be used for joint CMB and BBN analyses without requiring extensive computational resources, including on personal hardware.

Improving Radial Velocities by Marginalizing over Stars and Sky: Achieving 30 m/s RV Precision for APOGEE in the Plate Era
Andrew K. Saydjari, Douglas P. Finkbeiner, Adam J. Wheeler, Jon A. Holtzman, John C. Wilson, Andrew R. Casey, Sophia Sánchez-Maes, Joel R. Brownstein, David W. Hogg, Michael R. Blanton
[ arXiv:2408.07126 | code ]

Abstract The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of R∼22,500. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of the instrument. Here we present an RV catalog based on a new reduction of all 2.6 million visits of APOGEE DR17 and validate it against improved estimates for the theoretical RV performance. The core ideas of the new reduction are the simultaneous modeling of all components in the spectra, rather than a separate subtraction of point estimates for the sky, and a marginalization over stellar types, rather than a grid search for an optimum. We show that this catalog, when restricted to RVs measured with the same fiber, achieves noise-limited precision down to 30 m/s and delivers well-calibrated uncertainties. We also introduce a general method for calibrating fiber-to-fiber constant RV offsets and demonstrate its importance for high RV precision work in multi-fiber spectrographs. After calibration, we achieve 47 m/s RV precision on the combined catalog with RVs measured with different fibers. This degradation in precision relative to measurements with only a single fiber suggests that refining line spread function models should be a focus in SDSS-V to improve the fiber-unified RV catalog.

AT2023vto: An Exceptionally Luminous Helium Tidal Disruption Event from a Massive Star
Harsh Kumar, Edo Berger, Daichi Hiramatsu, Sebastian Gomez, Peter K. Blanchard, Yvette Cendes, K. Azalee Bostroem, Joseph Farah, Estefania Padilla Gonzalez, Andrew Howell, Curtis McCully, Megan Newsome, Giacomo Terreran
[ arXiv:2408.01482 ]

Abstract We present optical/UV observations and the spectroscopic classification of the transient AT2023vto as a tidal disruption event (TDE) at z = 0.4846. The spectrum is dominated by a broad He II λ4686 emission line, with a width of ~ 3.76×104 km/s and a blueshift of ~ 1.05×104 km/s, classifying it as a member of the TDE-He class. The light curve exhibits a long rise and decline timescale, with a large peak absolute magnitude of Mg ~ -23.6, making it the most luminous of the classical optical TDEs (H, H+He, He) discovered to date by about 2 mag (and ~ 4 mag compared to the mean of the population). The light curve exhibits a persistent blue color of g - r ~ -0.4 mag throughout its evolution, similar to other TDEs, but distinct from supernovae. We identify the host galaxy of AT2023vto in archival Pan-STARRS images and find that the transient is located at the galaxy center, and that its inferred central black hole mass is ~ 107 M⊙. Modeling the light curves of AT2023vto, we find that it resulted from the disruption of a ~ 9 M⊙ star by a ~107 M⊙ supermassive black hole. The star mass is about 5 times larger than the highest star masses previously inferred in TDEs, and the black hole mass is at the high end of the distribution. AT2023vto is comparable in luminosity and timescale to some putative TDEs (with a blue featureless continuum), as well as to the mean of the recently identified population of ambiguous nuclear transients (ANTs), although the latter are spectroscopically distinct and tend to have longer timescales. ANTs have been speculated to arise from tidal disruptions of massive stars, perhaps in active galactic nuclei, and AT2023vto may represent a similar case but in a dormant black hole, thereby bridging the TDE and ANT populations. We anticipate that Rubin Observatory / LSST will uncover similar luminous TDEs to z ~ 3.

On the Generality and Persistence of Cosmological Stasis
James Halverson, Sneh Pandya
Physical Review D, Vol. 110, Iss. 7, 2024 [ arXiv:2408.00835 | code ]

Abstract Hierarchical decays of N matter species to radiation may balance against Hubble expansion to yield stasis, a new phase of cosmological evolution with constant matter and radiation abundances. We analyze stasis with various machine learning techniques on the full 2N-dimensional space of decay rates and abundances, which serve as inputs to the system of Boltzmann equations that governs the dynamics. We construct a differentiable Boltzmann solver to maximize the number of stasis e-folds . High-stasis configurations obtained by gradient ascent motivate log-uniform distributions on rates and abundances to accompany power-law distributions of previous works. We demonstrate that random configurations drawn from these families of distributions regularly exhibit many e-folds of stasis. We additionally use them as priors in a Bayesian analysis conditioned on stasis, using stochastic variational inference with normalizing flows to model the posterior. All three numerical analyses demonstrate the generality of stasis and point to a new model in which the rates and abundances are exponential in the species index. We show that the exponential model solves the exact stasis equations, is an attractor, and satisfies ∝N, exhibiting inflation-level e-folding with a relatively low number of species. This is contrasted with the ∝log(N) scaling of power-law models. Finally, we discuss implications for the emergent string conjecture and string axiverse.

A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data
Nashwan Sabti, Ram Reddy, Julian B. Muñoz, Siddharth Mishra-Sharma, Taewook Youn
Machine Learning: Science and Technology, 2025, Volume 6, Number 1 [ arXiv:2407.21097 | code ]

Abstract Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast majority of modes, thereby degrading the quality of the data anisotropically. To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21-cm data lost to wedge filtering. Our method leverages the non-Gaussian nature of the 21-cm signal to effectively map wedge-filtered 3D lightcones to samples from the conditional distribution of wedge-recovered lightcones. We demonstrate how our method is able to restore spatial information effectively, considering both varying cosmological initial conditions and astrophysics. Furthermore, we discuss a number of future avenues where this approach could be applied in analyses of the 21-cm signal, potentially offering new opportunities to improve our understanding of the Universe during the epochs of cosmic dawn and reionization.

The Type I Superluminous Supernova Catalog I: Light Curve Properties, Models, and Catalog Description
Sebastian Gomez, Matt Nicholl, Edo Berger, Peter K. Blanchard, V. Ashley Villar, Sofia Rest, Griffin Hosseinzadeh, Aysha Aamer, Yukta Ajay, Wasundara Athukoralalage, David C. Coulter, Tarraneh Eftekhari, Achille Fiore, Noah Franz, Ori Fox, Alexander Gagliano, Daichi Hiramatsu, D. Andrew Howell, Brian Hsu, Mitchell Karmen, Matthew R. Siebert, Réka Könyves-Tóth, Harsh Kumar, Curtis McCully, Craig Pellegrino, Justin Pierel, Armin Rest, Qinan Wang
Monthly Notices of the Royal Astronomical Society, 2024 [ arXiv:2407.07946 | code ]

Abstract We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of UV, optical and IR photometry from both publicly available data and our own FLEET observational follow-up program, totaling over 30,000 photometric detections. Using these data we derive observational parameters such as the peak absolute magnitudes, rise and decline timescales, as well as bolometric luminosities, temperature and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of 56Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at ≈6.5 M⊙. We find no significant redshift dependence for any parameter, and no evidence for distinct sub-types of SLSNe. We find that <3% of SLSNe are best fit with a significant contribution from radioactive decay ≳50%, representing a set of relatively dim and slowly declining SNe. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works. The complete catalog, including all of the photometry, models, and derived parameters, is made available as an open-source resource on GitHub.

Find the haystacks, then look for needles: The rate of strongly lensed transients in galaxy-galaxy strong gravitational lenses
Ana Sainz de Murieta, Thomas E. Collett, Mark R. Magee, Justin D. R. Pierel, Wolfgang J. R. Enzi, Martine Lokken, Alex Gagliano, Dan Ryczanowski
[ arXiv:2407.04080 ]

Abstract The time delay between appearances of multiple images of a gravitationally lensed supernova (glSN) is sensitive to the Hubble constant, H0. As well as time delays, a lensed host galaxy is needed to enable precise inference of H0. In this work we investigate the connection between discoverable lensed transients and their host galaxies. We find that LSST will discover 88 glSNe per year, of which 54% will also have a strongly lensed host. The rates can change by approximately 30 percent uncertainty depending primarily on the choice of unlensed SN population and uncertainties in the redshift evolution of the deflector population, but the fraction of glSNe with a lensed host is consistently around a half. LSST will discover 20 glSNe per year in systems that could plausibly have been identified by Euclid as galaxy-galaxy lenses before the discovery of the glSN. Such systems have preferentially longer time delays and therefore are well suited for cosmography. We define a golden sample of glSNe Ia with time delays over 10 days, image separations greater than 0.8 arcseconds, and a multiply imaged host. For this golden sample, we find 91% occur in systems that should already be discoverable as galaxy-galaxy lenses in Euclid. For cosmology with glSNe, monitoring Euclid lenses is a plausible alternative to searching the entire LSST alert stream.

Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform
Matthew Craigie, Peter L. Taylor, Yuan-Sen Ting, Carolina Cuesta-Lazaro, Rossana Ruggeri, Tamara M. Davis
[ arXiv:2405.13083 ]

Abstract Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simulation used to model the noise properties of the galaxy distribution. In a recent paper, we introduce an unsupervised learning approach which offers an alternative method that avoids the dependence on mock catalogs, by learning parity violation directly from observational data. However, the Convolutional Neural Network (CNN) model utilized by our previous unsupervised approach struggles to extend to more realistic scenarios where data is limited. We propose a novel method, the Neural Field Scattering Transform (NFST), which enhances the Wavelet Scattering Transform (WST) technique by adding trainable filters, parameterized as a neural field. We first tune the NFST model to detect parity violation in a simplified dataset, then compare its performance against WST and CNN benchmarks across varied training set sizes. We find the NFST can detect parity violation with 4× less data than the CNN and 32× less than the WST. Furthermore, in cases with limited data the NFST can detect parity violation with up to 6σ confidence, where the WST and CNN fail to make any detection. We identify that the added flexibility of the NFST, and particularly the ability to learn asymmetric filters, as well as the specific symmetries built into the NFST architecture, contribute to its improved performance over the benchmark models. We further demonstrate that the NFST is readily interpretable, which is valuable for physical applications such as the detection of parity violation.

Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
The Astrophysical Journal, 978, 64, 2025 [ arXiv:2405.05255 | code ]

Abstract Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The cornerstone of modern cosmology is the ability to generate predictions for observed astrophysical fields from theory and constrain physical models from observations using these predictions. This work uses a single diffusion generative model to address these interlinked objectives -- as a surrogate model or emulator for cold dark matter density fields conditional on input cosmological parameters, and as a parameter inference model that solves the inverse problem of constraining the cosmological parameters of an input field. The model is able to emulate fields with summary statistics consistent with those of the simulated target distribution. We then leverage the approximate likelihood of the diffusion generative model to derive tight constraints on cosmology by using the Hamiltonian Monte Carlo method to sample the posterior on cosmological parameters for a given test image. Finally, we demonstrate that this parameter inference approach is more robust to the addition of noise than baseline parameter inference networks.

A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics
Beyond-2pt Collaboration - Elisabeth Krause, Yosuke Kobayashi, Andrés N. Salcedo, Mikhail M. Ivanov, Tom Abel, Kazuyuki Akitsu, Raul E. Angulo, Giovanni Cabass, Sofia Contarini, Carolina Cuesta-Lazaro, ChangHoon Hahn, Nico Hamaus, Donghui Jeong, Chirag Modi, Nhat-Minh Nguyen, Takahiro Nishimichi, Enrique Paillas, Marcos Pellejero Ibañez, Oliver H. E. Philcox, Alice Pisani, Fabian Schmidt, Satoshi Tanaka, Giovanni Verza, Sihan Yuan, Matteo Zennaro
[ arXiv:2405.02252 ]

Abstract The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge 'Beyond-2pt', initiated during the Aspen 2022 Summer Program 'Large-Scale Structure Cosmology beyond 2-Point Statistics,' whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ('masked') cosmological parameters of the underlying ΛCDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented.

Multi-filter UV to NIR Data-driven Light Curve Templates for Stripped Envelope Supernovae
Somayeh Khakpash, Federica B. Bianco, Maryam Modjaz, Willow F. Fortino, Alexander Gagliano, Conor Larison, Tyler A. Pritchard
The Astrophysical Journal, Volume 275, Number 2, 2024 [ arXiv:2405.01672 ]

Abstract While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all their envelopes of Hydrogen and Helium, the photometric evolution of classes within this family is not fully characterized. Photometric surveys, like the Vera C. Rubin Legacy Survey of Space and Time, will discover tens of thousands of transients each night and spectroscopic follow-up will be limited, prompting the need for photometric classification and inference based solely on photometry. We have generated 54 data-driven photometric templates for SESNe of subtypes IIb, Ib, Ic, Ic-bl, and Ibn in U/u, B, g, V, R/r, I/i, J, H, Ks, and Swift w2, m2, w1 bands using Gaussian Processes and a multi-survey dataset composed of all well-sampled open-access light curves (165 SESNe, 29531 data points) from the Open Supernova Catalog. We use our new templates to assess the photometric diversity of SESNe by comparing final per-band subtype templates with each other and with individual, unusual and prototypical SESNe. We find that SNe Ibns and Ic-bl exhibit a distinctly faster rise and decline compared to other subtypes. We also evaluate the behavior of SESNe in the PLAsTiCC and ELAsTiCC simulations of LSST light curves highlighting differences that can bias photometric classification models trained on the simulated light curves. Finally, we investigate in detail the behavior of fast-evolving SESNe (including SNe Ibn) and the implications of the frequently observed presence of two peaks in their light curves.

SN 2024ggi in NGC 3621: Rising Ionization in a Nearby, CSM-Interacting Type II Supernova
CW. V. Jacobson-Galán, K. W. Davis, C. D. Kilpatrick, L. Dessart, R. Margutti, R. Chornock, R. J. Foley, P. Arunachalam, K. Auchettl, C. R. Bom, R. Cartier, D. A. Coulter, G. Dimitriadis, D. Dickinson, M. R. Drout, A. T. Gagliano, C. Gall, B. Garretson, L. Izzo, D. O. Jones, N. LeBaron, H.-Y. Miao, D. Milisavljevic, Y.-C. Pan, A. Rest, C. Rojas-Bravo, A. Santos, H. Sears, B. M. Subrayan, K. Taggart, S. Tinyanont
The Astrophysical Journal, 2024, Volume 972, Number 2 [ arXiv:2404.19006 ]

Abstract We present UV/optical/NIR observations and modeling of supernova (SN) 2024ggi, a type II supernova (SN II) located in NGC 3621 at 7.2 Mpc. Early-time ('flash') spectroscopy of SN 2024ggi within +0.8 days of discovery shows emission lines of H I, He I, C III, and N III with a narrow core and broad, symmetric wings (i.e., IIn-like) arising from the photoionized, optically-thick, unshocked circumstellar material (CSM) that surrounded the progenitor star at shock breakout. By the next spectral epoch at +1.5 days, SN 2024ggi showed a rise in ionization as emission lines of He II, C IV, N IV/V and O V became visible. This phenomenon is temporally consistent with a blueward shift in the UV/optical colors, both likely the result of shock breakout in an extended, dense CSM. The IIn-like features in SN 2024ggi persist on a timescale of tIIn=3.8±1.6 days at which time a reduction in CSM density allows the detection of Doppler broadened features from the fastest SN material. SN 2024ggi has peak UV/optical absolute magnitudes of Mw2=−18.7 mag and Mg=−18.1 mag that are consistent with the known population of CSM-interacting SNe II. Comparison of SN 2024ggi with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium (nLTE) radiative-transfer simulations suggests a progenitor mass-loss rate of M˙=10−2M⊙ yr−1 (vw = 50 km/s), confined to a distance of r<5×1014 cm. Assuming a wind velocity of vw = 50 km/s, the progenitor star underwent an enhanced mass-loss episode in the last ~3 years before explosion.

Learning Galaxy Intrinsic Alignment Correlations
Sneh Pandya, Yuanyuan Yang, Nicholas Van Alfen, Jonathan Blazek, Robin Walters
[ arXiv:2404.13702 ]

Abstract The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak lensing analyses, represents the correlation of galaxy shapes due to gravitational tidal interactions and galaxy formation processes. As such, understanding IA is paramount for accurate cosmological inferences from weak lensing surveys; however, one limitation to our understanding and mitigation of IA is expensive simulation-based modeling. In this work, we present a deep learning approach to emulate galaxy position-position (ξ), position-orientation (ω), and orientation-orientation (η) correlation function measurements and uncertainties from halo occupation distribution-based mock galaxy catalogs. We find strong Pearson correlation values with the model across all three correlation functions and further predict aleatoric uncertainties through a mean-variance estimation training procedure. ξ(r) predictions are generally accurate to ≤10%. Our model also successfully captures the underlying signal of the noisier correlations ω(r) and η(r), although with a lower average accuracy. We find that the model performance is inhibited by the stochasticity of the data, and will benefit from correlations averaged over multiple data realizations. Our code will be made open source upon journal publication.

Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams
P. D. Aleo, A. W. Engel, G. Narayan, C. R. Angus, K. Malanchev, K. Auchettl, V. F. Baldassare, A. Berres, T. J. L. de Boer, B. M. Boyd, K. C. Chambers, K. W. Davis, N. Esquivel, D. Farias, R. J. Foley, A. Gagliano, C. Gall, H. Gao, S. Gomez, M. Grayling, D. O. Jones, C.-C. Lin, E. A. Magnier, K. S. Mandel, T. Matheson, S. I. Raimundo, V. G. Shah, M. D. Soraisam, K. M. de Soto, S. Vicencio, V. A. Villar, R. J. Wainscoat
The Astrophysical Journal, Volume 974, Number 2, 2024 [ arXiv:2404.01235 ]

Abstract We present LAISS (Lightcurve Anomaly Identification and Similarity Search), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly ZTF Alert Stream via the ANTARES broker, identifying a manageable ∼1-5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host-galaxy features within a random forest classifier, tagging transients of rare classes (spectroscopic anomalies), of uncommon host-galaxy environments (contextual anomalies), and of peculiar or interaction-powered phenomena (behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host-galaxy environments. We use analogs for data-driven discovery, characterization, (re-)classification, and imputation in retrospective and real-time searches. To date we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to: SLSNe, TDEs, SNe IIn, SNe IIb, SNe Ia-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018-2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the "needle in the haystack" in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
Ethan Marx, William Benoit, Alec Gunny, Rafia Omer, Deep Chatterjee, Ricco C. Venterea, Lauren Wills, Muhammed Saleem, Eric Moreno, Ryan Raikman, Ekaterina Govorkova, Dylan Rankin, Michael W. Coughlin, Philip Harris, Erik Katsavounidis
[ arXiv:2403.18661 | code ]

Abstract The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies (O(1/,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes.

Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS
Victoria Ono, Core Francisco Park, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro, Francisco Villaescusa-Navarro
The Astrophysical Journal, 2024, Volume 970, Number 2 [ arXiv:2403.10648 | code ]

Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between dark matter density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in the galaxy formation models, that remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct dark matter fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and sub-grid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields, while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes approximately 500 times larger than those it was trained on, and across different galaxy formation models. Code for reproducing these results can be found at this https URL

Supernovae Time Profiles as a Probe of New Physics at Neutrino Telescopes
Jeff Lazar, Ying-Ying Li, Carlos A. Arguelles, Vedran Brdar
[ arXiv:2403.09781 | code ]

Abstract Neutrino telescopes, including IceCube, can detect galactic supernova events by observing the collective rise in photomultiplier count rates with a sub-second time resolution. Leveraging precise timing, we demonstrate the ability of neutrino telescopes to explore new weakly coupled states emitted from supernovae and subsequently decaying to neutrinos. Our approach utilizes publicly available packages, \texttt{ASTERIA} and \texttt{SNEWPY}, for simulating detector responses and parametrizing neutrino fluxes originating from Standard Model and new physics. We present results for two beyond the Standard Model scenarios and introduce the tool developed for testing a diverse range of new physics models.

PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models
Siddharth Mishra-Sharma, Yiding Song, Jesse Thaler
COLM 2024 [ arXiv:2403.08851 | code ]

Abstract We present PAPERCLIP (Proposal Abstracts Provide an Effective Representation for Contrastive Language-Image Pre-training), a method which associates astronomical observations imaged by telescopes with natural language using a neural network model. The model is fine-tuned from a pre-trained Contrastive Language-Image Pre-training (CLIP) model using successful observing proposal abstracts and corresponding downstream observations, with the abstracts optionally summarized via guided generation using large language models (LLMs). Using observations from the Hubble Space Telescope (HST) as an example, we show that the fine-tuned model embodies a meaningful joint representation between observations and natural language through tests targeting image retrieval (i.e., finding the most relevant observations using natural language queries) and description retrieval (i.e., querying for astrophysical object classes and use cases most relevant to a given observation). Our study demonstrates the potential for using generalist foundation models rather than task-specific models for interacting with astronomical data by leveraging text as an interface.

Superphot+: Realtime Fitting and Classification of Supernova Light Curves
Kaylee M. de Soto (1), Ashley Villar (1), Edo Berger (1 and 2), Sebastian Gomez (3), Griffin Hosseinzadeh (4), Doug Branton (5), Sandro Campos (6), Melissa DeLucchi (6), Jeremy Kubica (6), Olivia Lynn (6), Konstantin Malanchev (6), Alex I. Malz (6) ((1) Center for Astrophysics | Harvard & Smithsonian, (2) The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, (3) Space Telescope Science Institute, (4) Steward Observatory | University of Arizona, (5) DiRAC Institute and the Department of Astronomy | University of Washington, (6) McWilliams Center for Cosmology | Department of Physics at Carnegie Mellon University)
The Astrophysics Journal, Volume 974, Number 2, 2024 [ arXiv:2403.07975 | code ]

Abstract Photometric classifications of supernova (SN) light curves have become necessary to utilize the full potential of large samples of observations obtained from wide-field photometric surveys, such as the Zwicky Transient Facility (ZTF) and the Vera C. Rubin Observatory. Here, we present a photometric classifier for SN light curves that does not rely on redshift information and still maintains comparable accuracy to redshift-dependent classifiers. Our new package, Superphot+, uses a parametric model to extract meaningful features from multiband SN light curves. We train a gradient-boosted machine with fit parameters from 6,061 ZTF SNe that pass data quality cuts and are spectroscopically classified as one of five classes: SN Ia, SN II, SN Ib/c, SN IIn, and SLSN-I. Without redshift information, our classifier yields a class-averaged F1-score of 0.61 +/- 0.02 and a total accuracy of 0.83 +/- 0.01. Including redshift information improves these metrics to 0.71 +/- 0.02 and 0.88 +/- 0.01, respectively. We assign new class probabilities to 3,558 ZTF transients that show SN-like characteristics (based on the ALeRCE Broker light curve and stamp classifiers), but lack spectroscopic classifications. Finally, we compare our predicted SN labels with those generated by the ALeRCE light curve classifier, finding that the two classifiers agree on photometric labels for 82 +/- 2% of light curves with spectroscopic labels and 72% of light curves without spectroscopic labels. Superphot+ is currently classifying ZTF SNe in real time via the ANTARES Broker, and is designed for simple adaptation to six-band Rubin light curves in the future.

Full-shape analysis with simulation-based priors: constraints on single field inflation from BOSS
Mikhail M. Ivanov, Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma, Andrej Obuljen, Michael W. Toomey
Physical Review D, 2024, Volume 110, Issue 6 [ arXiv:2402.13310 | code ]

Abstract We present an efficient approach to set informative physically motivated priors for EFT-based full-shape analyses of galaxy survey data. We extract these priors from simulated galaxy catalogs based on halo occupation distribution (HOD) models. As a first step, we build a joint distribution between EFT galaxy bias and HOD parameters from a set of 10,500 HOD mock catalogs. We make use of the field level EFT technique that allows for cosmic variance cancellation, enabling a precision calibration of EFT parameters from computationally inexpensive small-volume simulations. As a second step, we use neural density estimators -- normalizing flows -- to model the marginal probability density of the EFT parameters, which can be used as a prior distribution in full shape analyses. As a first application, we use our HOD-based prior distribution in a new analysis of galaxy power spectra and bispectra from the BOSS survey in the context of single field primordial non-Gaussianity. We find that our approach leads to a reduction of the posterior volume of bias parameters by an order of magnitude. We also find fequilNL=650±310 and forthoNL=42±130 (at 68\% CL) in a combined two-template analysis, representing a ≈40% improvement in constraints on single field primordial non-Gaussianity, equivalent to doubling the survey volume.

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
The Open Journal of Astrophysics, 2024, Volume 7 [ arXiv:2402.05137 | code ]

Abstract This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at this https URL: https://github.com/maho3/ltu-ili.

Equivariant Symmetry Breaking Sets
YuQing Xie, Tess Smidt
[ arXiv:2402.02681 ]

Abstract Equivariant neural networks (ENNs) have been shown to be extremely effective in applications involving underlying symmetries. By construction ENNs cannot produce lower symmetry outputs given a higher symmetry input. However, spontaneous symmetry breaking occurs in many physical systems and we may obtain a less symmetric stable state from an initial highly symmetric one. Hence, it is imperative that we understand how to systematically break symmetry in ENNs. In this work, we propose a novel symmetry breaking framework that is fully equivariant. We emphasize that our approach is general and applicable to equivariance under any group. To achieve this, we introduce the idea of symmetry breaking sets (SBS). Rather than redesign existing networks, we design sets of symmetry breaking objects which we feed into our network based on the symmetry of our inputs and outputs. We show there is a natural way to define equivariance on these sets, which gives an additional constraint. Minimizing the size of these sets equates to data efficiency. We prove that minimizing these sets translates to a well studied group theory problem, and tabulate solutions to this problem for the point groups. Finally, we provide some examples of symmetry breaking to demonstrate how our approach works in practice.

Substructure Detection in Realistic Strong Lensing Systems with Machine LearningSubstructure Detection in Realistic Strong Lensing Systems with Machine Learning
Arthur Tsang, Atınç Çağan Şengül, Cora Dvorkin
[ arXiv:2401.16624 ]

Abstract Tens of thousands of galaxy-galaxy strong lensing systems are expected to be discovered by the end of the decade. These will form a vast new dataset that can be used to probe subgalactic dark matter structures through its gravitational effects, which will in turn allow us to study the nature of dark matter at small length scales. This work shows how we can leverage machine learning to search through the data and identify which systems are most likely to contain dark matter substructure and thus can be studied in greater depth. We use a UNet, an image segmentation architecture, on a simulated strongly-lensed dataset with realistic sources (COSMOS galaxies), lenses (power-law elliptical profiles with multipoles and external shear), and noise. Our machine learning algorithm is able to quickly detect most substructure at high image resolution and subhalo concentration. At a false positive rate of 10%, we are able to identify systems with substructure at a true positive rate of 71% for a subhalo mass range of 109-109.5M⊙. While recent detections are consistent with higher concentrations, we find that our algorithm fails at detecting subhalos with lower concentrations (expected from ΛCDM simulations).

Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources
Víctor Samuel Pérez-Díaz, Juan Rafael Martínez-Galarza, Alexander Caicedo, Raffaele D’Abrusco
Monthly Notices of the Royal Astronomical Society 2024, Volume 528, Issue 3 [ arXiv:2401.12203 | code ]

Abstract The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for population studies, as well as for anomaly detection, i.e., the identification of new unexplored phenomena, including transients and spectrally extreme sources. Despite the importance of this task, classification remains challenging in X-ray astronomy due to the lack of optical counterparts and representative training sets. We develop an alternative methodology that employs an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources with a limited number of labeled sources, and without ancillary information from optical and infrared catalogs. We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections, and demonstrate the success of the method at identifying emission from young stellar objects, as well as distinguishing between small-scale and large-scale compact accretors with a significant level of confidence. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses such as the unified AGN model. This provides interpretability to the probabilistic classifier. Code and tables are available publicly through GitHub. We provide a web playground for readers to explore our final classification at https://umlcaxs-playground.streamlit.app.

A Physics-Informed Variational Autoencoder for Rapid Galaxy Inference and Anomaly Detection
Alexander Gagliano, V. Ashley Villar
[ arXiv:2312.16687 | code ]

Abstract The Vera C. Rubin Observatory is slated to observe nearly 20 billion galaxies during its decade-long Legacy Survey of Space and Time. The rich imaging data it collects will be an invaluable resource for probing galaxy evolution across cosmic time, characterizing the host galaxies of transient phenomena, and identifying novel populations of anomalous systems. To facilitate these studies, we introduce a convolutional variational autoencoder trained to estimate the redshift, stellar mass, and star-formation rates of galaxies from multi-band imaging data. We train and test our physics-informed CVAE on a spectroscopic sample of ∼26,000 galaxies within z<1 imaged through the Dark Energy Camera Legacy Survey. We show that our model can infer redshift and stellar mass more accurately than the latest image-based self-supervised learning approaches, and is >100x faster than more computationally-intensive SED-fitting techniques. Using a small sample of Green Pea and Red Spiral galaxies reported in the literature, we further demonstrate how this CVAE can be used to rapidly identify rare galaxy populations and interpret what makes them unique.

Inhomogeneous energy injection in the 21-cm power spectrum: Sensitivity to dark matter decay
Yitian Sun, Joshua W. Foster, Hongwan Liu, Julian B. Muñoz, Tracy R. Slatyer
Physical Review D, Vol. 111 Iss. 4, 2025 [ arXiv:2312.11608 | code ]

Abstract The 21-cm signal provides a novel avenue to measure the thermal state of the universe during cosmic dawn and reionization (redshifts z∼5−30), and thus to probe energy injection from decaying or annihilating dark matter (DM). These DM processes are inherently inhomogeneous: both decay and annihilation are density dependent, and furthermore the fraction of injected energy that is deposited at each point depends on the gas ionization and density, leading to further anisotropies in absorption and propagation. In this work, we develop a new framework for modeling the impact of spatially inhomogeneous energy injection and deposition during cosmic dawn, accounting for ionization and baryon density dependence, as well as the attenuation of propagating photons. We showcase how this first completely inhomogeneous treatment affects the predicted 21-cm power spectrum in the presence of exotic sources of energy injection, and forecast the constraints that upcoming HERA measurements of the 21-cm power spectrum will set on DM decays to photons and to electron/positron pairs. These projected constraints considerably surpass those derived from CMB and Lyman-α measurements, and for decays to electron/positron pairs they exceed all existing constraints in the sub-GeV mass range, reaching lifetimes of ∼1028s. Our analysis demonstrates the unprecedented sensitivity of 21-cm cosmology to exotic sources of energy injection during the cosmic dark ages. Our code, DM21cm, includes all these effects and is publicly available in an accompanying release.

Cosmological Field Emulation and Parameter Inference with Diffusion Models
Nayantara Mudur, Carolina Cuesta-Lazaro, Douglas P. Finkbeiner
[ arXiv:2312.07534 ]

Abstract Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields. We leverage diffusion generative models to address two tasks of importance to cosmology -- as an emulator for cold dark matter density fields conditional on input cosmological parameters Ωm and σ8, and as a parameter inference model that can return constraints on the cosmological parameters of an input field. We show that the model is able to generate fields with power spectra that are consistent with those of the simulated target distribution, and capture the subtle effect of each parameter on modulations in the power spectrum. We additionally explore their utility as parameter inference models and find that we can obtain tight constraints on cosmological parameters.

What does cosmology teach us about non-gravitational properties of dark matter?
Tracy R. Slatyer
Nuclear Physics B, 2024, Volume 1003 [ ]

Abstract Cosmological observations provide our most robust evidence for dark matter that is (approximately) collisionless and cold, and furthermore can provide powerful tests of the non-gravitational properties of dark matter. There are exciting prospects for significant experimental/observational progress in the coming years. In particular, current experiments are targeting a first confirmed detection of primordial 21 cm radiation and a measurement of its power spectrum, which would open a new observational window on the end of the cosmic dark ages and cosmic dawn. On a longer timescale, there are proposed missions that could improve our measurements of the energy spectrum of the cosmic microwave background radiation by 3+ orders of magnitude, providing a new physical probe of the thermal history of the universe up to keV temperatures. In this contribution, I will discuss how signals from dark matter interactions with Standard Model particles, in particular through annihilation and decay of particle-like dark matter, could appear in these observables, and recent improvements in their theoretical modeling. There are existing stringent and broadly applicable limits on annihilating and decaying dark matter (especially at sub-GeV mass scales) from the cosmic microwave background, and complementary and competitive bounds from the Lyman-α forest for leptonically decaying light dark matter. I will outline how energy injections that are not currently excluded could change the conditions of the early universe, impact the formation of the first stars and black hole seeds, and imprint signals in the cosmological background radiation.

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies
Nicolas Payot, Pablo Lemos, Laurence Perreault-Levasseur, Carolina Cuesta-Lazaro, Chirag Modi, Yashar Hezaveh
[ arXiv:2311.18017 ]

Abstract Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used to learn an effective evolution equation for the particles, by correcting the errors of the particle-mesh potential incurred on small scales during simulations. We find that our learnt correction yields evolution equations that generalize well to new, unseen initial conditions and cosmologies. We further demonstrate that the resulting corrected maps can be used in a simulation-based inference framework to yield an unbiased inference of cosmological parameters. The model, a network implemented in Fourier space, is exclusively trained on the particle positions and velocities.

A point cloud approach to generative modeling for galaxy surveys at the field level
Carolina Cuesta-Lazaro, Siddharth Mishra-Sharma
[ arXiv:2311.17141 | code ]

Abstract We introduce a diffusion-based generative model to describe the distribution of galaxies in our Universe directly as a collection of points in 3-D space (coordinates) optionally with associated attributes (e.g., velocities and masses), without resorting to binning or voxelization. The custom diffusion model can be used both for emulation, reproducing essential summary statistics of the galaxy distribution, as well as inference, by computing the conditional likelihood of a galaxy field. We demonstrate a first application to massive dark matter haloes in the Quijote simulation suite. This approach can be extended to enable a comprehensive analysis of cosmological data, circumventing limitations inherent to summary statistic -- as well as neural simulation-based inference methods.

Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Core Francisco Park, Victoria Ono, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro
[ arXiv:2311.08558 | code ]

Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. The relationship between dark matter density fields and galaxy distributions can be sensitive to assumptions in cosmology and astrophysical processes embedded in the galaxy formation models, that remain uncertain in many aspects. Based on state-of-the-art galaxy formation simulation suites with varied cosmological parameters and sub-grid astrophysics, we develop a diffusion generative model to predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields, while being able to marginalize over the uncertainties in cosmology and galaxy formation.

Pairing-based graph neural network for simulating quantum materials
Di Luo, David D. Dai, Liang Fu
[ arXiv:2311.02143 ]

Abstract We introduce a pairing-based graph neural network, GemiNet, for simulating quantum many-body systems. Our architecture augments a BCS mean-field wavefunction with a generalized pair amplitude parameterized by a graph neural network. Variational Monte Carlo with GemiNet simultaneously provides an accurate, flexible, and scalable method for simulating many-electron systems. We apply GemiNet to two-dimensional semiconductor electron-hole bilayers and obtain highly accurate results on a variety of interaction-induced phases, including the exciton Bose-Einstein condensate, electron-hole superconductor, and bilayer Wigner crystal. Our study demonstrates the potential of physically-motivated neural network wavefunctions for quantum materials simulations.

E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification
Sneh Pandya, Purvik Patel, Franc O, Jonathan Blazek
[ arXiv:2311.01500 | code ]

Abstract We propose the use of group convolutional neural network architectures (GCNNs) equivariant to the 2D Euclidean group, E(2), for the task of galaxy morphology classification by utilizing symmetries of the data present in galaxy images as an inductive bias in the architecture. We conduct robustness studies by introducing artificial perturbations via Poisson noise insertion and one-pixel adversarial attacks to simulate the effects of limited observational capabilities. We train, validate, and test GCNNs equivariant to discrete subgroups of E(2) - the cyclic and dihedral groups of order N - on the Galaxy10 DECals dataset and find that GCNNs achieve higher classification accuracy and are consistently more robust than their non-equivariant counterparts, with an architecture equivariant to the group D16 achieving a 95.52±0.18% test-set accuracy. We also find that the model loses <6% accuracy on a 50%-noise dataset and all GCNNs are less susceptible to one-pixel perturbations than an identically constructed CNN..

Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform
Georgios Valogiannis, Sihan Yuan, Cora Dvorkin
Physical Review D 2024, Volume 109, Issue 10 [ arXiv:2310.16116 ]

Abstract We perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the Wavelet Scattering Transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of extsc{AbacusSummit} simulations combined with a flexible Halo Occupation Distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range 0.46<z<0.57. We find that a joint WST + 2-point correlation function likelihood analysis allows us to obtain marginalized 1σ errors on the ΛCDM parameters that are tighter by a factor of 2.5−6, compared to the 2-point correlation function, and by a factor of 1.4−2.5 compared to the WST-only results. This corresponds to a competitive 0.9%, 2.3% and 1% level of determination for parameters ωc, σ8 & ns, respectively, and also to a 0.7% & 2.5% constraint on derived parameters h and f(z)σ8(z), in agreement with the extit{Planck} 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming Stage-IV spectroscopic observations.

Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample
Enrique Paillas, Carolina Cuesta-Lazaro, Will J. Percival, Seshadri Nadathur, Yan-Chuan Cai, Sihan Yuan, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16541 | code ]

Abstract We present a clustering analysis of the BOSS DR12 CMASS galaxy sample, combining measurements of the galaxy two-point correlation function and density-split clustering down to a scale of 1h−1Mpc. Our theoretical framework is based on emulators trained on high-fidelity mock galaxy catalogues that forward model the cosmological dependence of the clustering statistics within an extended-ΛCDM framework, including redshift-space and Alcock-Paczynski distortions. Our base-ΛCDM analysis finds ωcdm=0.1201±0.0022, σ8=0.792±0.034, and ns=0.970±0.018, corresponding to fσ8=0.462±0.020 at z≈0.525, which is in agreement with Planck 2018 predictions and various clustering studies in the literature. We test single-parameter extensions to base-ΛCDM, varying the running of the spectral index, the dark energy equation of state, and the density of massless relic neutrinos, finding no compelling evidence for deviations from the base model. We model the galaxy-halo connection using a halo occupation distribution framework, finding signatures of environment-based assembly bias in the data. We validate our pipeline against mock catalogues that match the clustering and selection properties of CMASS, showing that we can recover unbiased cosmological constraints even with a volume 84 times larger than the one used in this study.

SUNBIRD: A simulation-based model for full-shape density-split clustering
Carolina Cuesta-Lazaro, Enrique Paillas, Sihan Yuan, Yan-Chuan Cai, Seshadri Nadathur, Will J. Percival, Florian Beutler, Arnaud de Mattia, Daniel Eisenstein, Daniel Forero-Sanchez, Nelson Padilla, Mathilde Pinon, Vanina Ruhlmann-Kleider, Ariel G. Sánchez, Georgios Valogiannis, Pauline Zarrouk
[ arXiv:2309.16539 | code ]

Abstract Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-ΛCDM framework, incorporating the effects of redshift-space, Alcock-Paczynski distortions and models of the halo-galaxy connection. Our models reach sub-percent level accuracy down to 1h−1Mpc and are robust against different choices of galaxy-halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ωcdm, σ8, and ns by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters. Our code is made publicly available on Github.

Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
Mayeul Aubin, Carolina Cuesta-Lazaro, Ethan Tregidga, Javier Viaña, Cecilia Garraffo, Iouli E. Gordon, Mercedes López-Morales, Robert J. Hargreaves, Vladimir Yu. Makhnev, Jeremy J. Drake, Douglas P. Finkbeiner, Phillip Cargile
[ arXiv:2309.09337 | code ]

Abstract Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics. This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023, where one of the models secured the top position among 293 competitors. Leveraging Normalizing Flows, our models predict the posterior probability distribution of atmospheric parameters under different atmospheric assumptions. Moreover, we introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge. These findings highlight the need to reevaluate the evaluation metric and prompt further exploration of more efficient and accurate approaches for exoplanet atmosphere spectra analysis. Finally, we present recommendations to enhance the challenge and models, providing valuable insights for future applications on real observational data. These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds.

Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation
Gemma Zhang, Atınç Çağan Şengül, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2024, Volume 527, Issue 2 [ arXiv:2308.09739 | code ]

Abstract Examining the properties of subhalos with strong gravitational lensing images can shed light on the nature of dark matter. From upcoming large-scale surveys, we expect to discover orders of magnitude more strong lens systems that can be used for subhalo studies. To optimally extract information from a large number of strong lensing images, machine learning provides promising avenues for efficient analysis that is unachievable with traditional analysis methods, but application of machine learning techniques to real observations is still limited. We build upon previous work, which uses a neural likelihood-ratio estimator, to constrain the effective density slopes of subhalos and demonstrate the feasibility of this method on real strong lensing observations. To do this, we implement significant improvements to the forward simulation pipeline and undertake careful model evaluation using simulated images. Ultimately, we use our trained model to predict the effective subhalo density slope from combining a set of strong lensing images taken by the extit{Hubble Space Telescope}. We found the subhalo slope measurement of this set of observations to be steeper than the slope predictions of cold dark matter subhalos. Our result adds to several previous works that also measured high subhalo slopes in observations. Although a possible explanation for this is that subhalos with steeper slopes are easier to detect due to selection effects and thus contribute to statistical bias, our result nevertheless points to the need for careful analysis of more strong lensing observations from future surveys.

Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
Aizhan Akhmetzhanova, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2023, Volume 527, Issue 3 [ arXiv:2308.09751 ]

Abstract The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information. We introduce a method that leverages the paradigm of self-supervised machine learning in a novel manner to construct representative summaries of massive datasets using simulation-based augmentations. Deploying the method on hydrodynamical cosmological simulations, we show that it can deliver highly informative summaries, which can be used for a variety of downstream tasks, including precise and accurate parameter inference. We demonstrate how this paradigm can be used to construct summary representations that are insensitive to prescribed systematic effects, such as the influence of baryonic physics. Our results indicate that self-supervised machine learning techniques offer a promising new approach for compression of cosmological data as well its analysis.

An Extensive Hubble Space Telescope Study of the Offset and Host Light Distributions of Type I Superluminous Supernovae
Brian Hsu, Peter K. Blanchard, Edo Berger, Sebastian Gomez
The Astrophysical Journal 2024, Volume 961, Number 2 [ arXiv:2308.07271 ]

Abstract We present an extensive Hubble Space Telescope (HST) rest-frame ultraviolet (UV) imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift range z≈0.05−2. Using precise astrometric matching with SN images, we determine the distributions of physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distribution. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of 1.5−4 times their host half-light radius. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and II SNe. Furthermore, we find that about 40\% of all SLSNe occur in the dimmest regions of their host galaxies (fractional flux of 0), in stark contrast to LGRBs and Type Ib/c and II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical lights curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of ∼102 km s−1.

A Parsec-Scale Galactic 3D Dust Map out to 1.25 kpc from the Sun
Gordian Edenhofer, Catherine Zucker, Philipp Frank, Andrew K. Saydjari, Joshua S. Speagle, Douglas Finkbeiner, Torsten Enßlin
Astronomy & Astrophysics, Forthcoming article, 2024, Section Interstellar and circumstellar matter [ arXiv:2308.01295 ]

Abstract High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of 1.25 kpc from the Sun. We leverage distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we infer the spatial distribution of dust extinction. We model the logarithmic dust extinction with a Gaussian Process in a spherical coordinate system via Iterative Charted Refinement and a correlation kernel inferred in previous work. We probe our 661 million dimensional posterior distribution using the variational inference method MGVI. Our 3D dust map achieves an angular resolution of 14' (Nside = 256). We sample the dust extinction in 516 distance bins spanning 69 pc to 1250 pc. We obtain a maximum distance resolution of 0.4 pc at 69 pc and a minimum distance resolution of 7 pc at 1.25 kpc. Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations.

From Discovery to the First Month of the Type II Supernova 2023ixf: High and Variable Mass Loss in the Final Year before Explosion
Daichi Hiramatsu, Daichi Tsuna, Edo Berger, Koichi Itagaki, Jared A. Goldberg, Sebastian Gomez, Kishalay De, Griffin Hosseinzadeh, K. Azalee Bostroem, Peter J. Brown, Iair Arcavi, Allyson Bieryla, Peter K. Blanchard, Gilbert A. Esquerdo, Joseph Farah, D. Andrew Howell, Tatsuya Matsumoto, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Jaehyon Rhee, Giacomo Terreran, József Vinkó, J. Craig Wheeler
The Astrophysical Journal Letters 2023, Volume 955, Number 1 [ arXiv:2307.03165 ]

Abstract We present the discovery of the Type II supernova SN 2023ixf in M101 and follow-up photometric and spectroscopic observations, respectively, in the first month and week of its evolution. Our discovery was made within a day of estimated first light, and the following light curve is characterized by a rapid rise (≈5 days) to a luminous peak (MV≈−18.2 mag) and plateau (MV≈−17.6 mag) extending to 30 days with a fast decline rate of ≈0.03 mag day−1. During the rising phase, U−V color shows blueward evolution, followed by redward evolution in the plateau phase. Prominent flash features of hydrogen, helium, carbon, and nitrogen dominate the spectra up to ≈5 days after first light, with a transition to a higher ionization state in the first ≈2 days. Both the U−V color and flash ionization states suggest a rise in the temperature, indicative of a delayed shock breakout inside dense circumstellar material (CSM). From the timescales of CSM interaction, we estimate its compact radial extent of ∼(3−7)×1014 cm. We then construct numerical light-curve models based on both continuous and eruptive mass-loss scenarios shortly before explosion. For the continuous mass-loss scenario, we infer a range of mass-loss history with 0.1−1.0M⊙yr−1 in the final 2−1 yr before explosion, with a potentially decreasing mass loss of 0.01−0.1M⊙yr−1 in ∼0.7−0.4 yr toward the explosion. For the eruptive mass-loss scenario, we favor eruptions releasing 0.3−1M⊙ of the envelope at about a year before explosion, which result in CSM with mass and extent similar to the continuous scenario. We discuss the implications of the available multiwavelength constraints obtained thus far on the progenitor candidate and SN 2023ixf to our variable CSM models.

Multiple Peaks and a Long Precursor in the Type IIn Supernova 2021qqp: An Energetic Explosion in a Complex Circumstellar Environment
Daichi Hiramatsu, Tatsuya Matsumoto, Edo Berger, Conor Ransome, V. Ashley Villar, Sebastian Gomez, Yvette Cendes, Kishalay De, K. Azalee Bostroem, Joseph Farah, D. Andrew Howell, Curtis McCully, Megan Newsome, Estefania Padilla Gonzalez, Craig Pellegrino, Akihiro Suzuki, Giacomo Terreran
The Astrophysical Journal, 2024, Volume 964, Number 2 [ arXiv:2305.11168 ]

Abstract We present optical photometry and spectroscopy of the Type IIn supernova (SN) 2021qqp. Its unusual light curve is marked by a long precursor for ≈300 days, a rapid increase in brightness for ≈60 days, and then a sharp increase of ≈1.6 mag in only a few days to a first peak of Mr≈−19.5 mag. The light curve then declines rapidly until it re-brightens to a second distinct peak of Mr≈−17.3 mag centered at ≈335 days after the first peak. The spectra are dominated by Balmer lines with a complex morphology, including a narrow component with a width of ≈1300 km s−1 (first peak) and ≈2500 km s−1 (second peak) that we associate with the circumstellar medium (CSM) and a P Cygni component with an absorption velocity of ≈8500 km s−1 (first peak) and ≈5600 km s−1 (second peak) that we associate with the SN-CSM interaction shell. Using the luminosity and velocity evolution, we construct a flexible analytical model, finding two significant mass-loss episodes with peak mass loss rates of ≈10 and ≈5M⊙ yr−1 about 0.8 and 2 yr before explosion, respectively, with a total CSM mass of ≈2−4M⊙. We show that the most recent mass-loss episode could explain the precursor for the year preceding the explosion. The SN ejecta mass is constrained to be ≈5−30M⊙ for an explosion energy of ≈(3−10)×1051 erg. We discuss eruptive massive stars (luminous blue variable, pulsational pair instability) and an extreme stellar merger with a compact object as possible progenitor channels.

Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2
David Shih, Matthew R. Buckley, Lina Necib
[ arXiv:2303.01529 ]

Abstract We present an update to Via Machinae, an automated stellar stream-finding algorithm based on the deep learning anomaly detector ANODE. Via Machinae identifies stellar streams within Gaia, using only angular positions, proper motions, and photometry, without reference to a model of the Milky Way potential for orbit integration or stellar distances. This new version, Via Machinae 2.0, includes many improvements and refinements to nearly every step of the algorithm, that altogether result in more robust and visually distinct stream candidates than our original formulation. In this work, we also provide a quantitative estimate of the false positive rate of Via Machinae 2.0 by applying it to a simulated Gaia-mock catalog based on Galaxia, a smooth model of the Milky Way that does not contain substructure or stellar streams. Finally, we perform the first full-sky search for stellar streams with Via Machinae 2.0, identifying 102 streams at high significance within the Gaia Data Release 2, of which only 10 have been previously identified. While follow-up observations for further confirmation are required, taking into account the false positive rate presented in this work, we expect approximately 90 of these stream candidates to correspond to real stellar structures.

Learning Silhouettes with Group Sparse Autoencoders
Emmanouil Theodosis and Demba Ba
Harvard CRISP Preprint [ ]

Abstract Sparse coding has been extensively used in neuroscience to model brain-like computation by drawing analogues between neurons’ firing activity and the nonzero elements of sparse vectors. Contemporary deep learning architectures have been used to model neural activity, inspired by signal processing algorithms; however sparse coding architectures are not able to explain the higher-order categorization that has been em- pirically observed at the neural level. In this work, we pro- pose a novel model-based architecture, termed group-sprase autoencoder, that produces sparse activity patterns in line with neural modeling, but showcases a higher-level order in its ac- tivation maps. We evaluate a dense model of our architecture on MNIST and CIFAR-10 and show that it learns dictionar- ies that resemble silhouettes of the given class, while its ac- tivations have a significantly higher level order compared to sparse architectures.

First demonstration of neural sensing and control in a kilometer-scale gravitational wave observatory
Nikhil Mukund, James Lough, Aparna Bisht, Holger Wittel, Séverin Landry Nadji, Christoph Affeldt, Fabio Bergamin, Marc Brinkmann, Volker Kringel, Harald Lück, Michael Weinert, Karsten Danzmann
Physical Review Applied, 2023, Volume 20, Issue 6 [ arXiv:2301.06221 ]

Abstract Suspended optics in gravitational wave (GW) observatories are susceptible to alignment perturbations, particularly slow drifts over time, due to variations in temperature and seismic levels. Such misalignments affect the coupling of the incident laser beam into the optical cavities, degrade both circulating power and optomechanical photon squeezing and thus decrease the astrophysical sensitivity to merging binaries. Traditional alignment techniques involve differential wavefront sensing using multiple quadrant photodiodes but are often restricted in bandwidth and are limited by the sensing noise. We present the first-ever successful implementation of neural network-based sensing and control at a gravitational wave observatory and demonstrate low-frequency control of the signal recycling mirror at the GEO 600 detector. Alignment information for three critical optics is simultaneously extracted from the interferometric dark port camera images via a CNN-LSTM network architecture and is then used for MIMO control using soft actor-critic-based deep reinforcement learning. Overall sensitivity improvement achieved using our scheme demonstrates deep learning's capabilities as a viable tool for real-time sensing and control for current and next-generation GW interferometers.

Non-parametric Lagrangian biasing from the insights of neural nets
Xiaohan Wu, Julian B. Munoz, Daniel J. Eisenstein
Journal of Cosmology and Astroparticle Physics 2023, Volume 2023 [ arXiv:2212.08095 ]

Abstract We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field. By fitting the mass-weighted halo field in the AbacusSummit simulations at z=0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2-5% underestimation of the large-scale power and can cause the neural net to overfit. We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space. Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales. Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.

Stellar Reddening Based Extinction Maps for Cosmological Applications
Nayantara Mudur, Core Francisco Park, Douglas P Finkbeiner
The Astrophysical Journal, 2023, Volume 949, Number 2 [ arXiv:2212.04514 | code ]

Abstract Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation functions between foreground lenses and background objects and the primordial non-gaussianity parameter fNL. Emission-based maps are contaminated by the cosmic infrared background, while maps inferred from stellar-reddenings suffer from imperfect removal of quasars and galaxies from stellar catalogs. Thus, stellar-reddening based maps using catalogs without extragalactic objects offer a promising path to making dust maps with minimal correlations with large-scale structure. We present two high-latitude integrated extinction maps based on stellar reddenings, with a point spread function of full-width half-maximum 6.1' and 15'. We employ a strict selection of catalog objects to filter out galaxies and quasars and measure the spatial correlation of our extinction maps with extragalactic structure. Our galactic extinction maps have reduced spatial correlation with large scale structure relative to most existing stellar-reddening based and emission-based extinction maps.

Measuring the 8621 Å Diffuse Interstellar Band in Gaia DR3 RVS Spectra: Obtaining a Clean Catalog by Marginalizing over Stellar Types
Andrew K. Saydjari, Catherine Zucker, J. E. G. Peek, Douglas P. Finkbeiner
The Astrophysical Journal, 954 141, 2023 [ arXiv:2212.03879 | code ]

Abstract Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with full posteriors on the joint distribution of the components. This decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data-space (the observed spectrum) -- a method we call "Marginalized Analytic Data-space Gaussian Inference for Component Separation" (MADGICS). We use a data-driven prior for the stellar component, which avoids missing stellar features not included in synthetic line lists. This technique provides statistically rigorous uncertainties and detection thresholds, which are required to work in the low signal-to-noise regime that is commonplace for dusty lines of sight. We reprocess all public Gaia DR3 RVS spectra and present an improved 8621 Å DIB catalog, free of detectable stellar line contamination. We constrain the rest-frame wavelength to 8623.14±0.087 Å (vacuum), find no significant evidence for DIBs in the Local Bubble from the 1/6th of RVS spectra that are public, and show unprecedented correlation with kinematic substructure in Galactic CO maps. We validate the catalog, its reported uncertainties, and biases using synthetic injection tests. We believe MADGICS provides a viable path forward for large-scale spectral line measurements in the presence of complex spectral contamination.

Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Nayantara Mudur, Douglas P. Finkbeiner
[ arXiv:2211.12444 | code ]

Abstract Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust. We examine the fidelity of the sampled cosmological fields relative to the true fields using three different metrics, and identify potential issues to address. We demonstrate a proof-of-concept application of the model trained on dust in denoising dust images. To our knowledge, this is the first application of this class of models to the interstellar medium.

Limits on Simultaneous and Delayed Optical Emission from Well-localized Fast Radio Bursts
Daichi Hiramatsu, Edo Berger, Brian D. Metzger, Sebastian Gomez, Allyson Bieryla, Iair Arcavi, D. Andrew Howell, Ryan Mckinven, Nozomu Tominaga
The Astrophysical Journal Letters 2023, volume 947, number 2 [ arXiv:2211.03974 ]

Abstract We present the largest compilation to date of optical observations during and following fast radio bursts (FRBs). The data set includes our dedicated simultaneous and follow-up observations, as well as serendipitous archival survey observations, for a sample of 15 well-localized FRBs: eight repeating and seven one-off sources. Our simultaneous (and nearly simultaneous with a 0.4 s delay) optical observations of 13 (1) bursts from the repeating FRB 20220912A provide the deepest such limits to date for any extragalactic FRB, reaching a luminosity limit of νLν≲1042 erg s−1 (≲2×1041 erg s−1) with 15−400 s exposures; an optical-flux-to-radio-fluence ratio of fopt/Fradio≲10−7 ms−1 (≲10−8 ms−1); and flux ratio of fopt/fradio≲0.02−≲2×10−5 (≲10−6) on millisecond to second timescales. These simultaneous limits provide useful constraints in the context of FRB emission models, such as the pulsar magnetosphere and pulsar nebula models. Interpreting all available optical limits in the context of the synchrotron maser model, we find that they constrain the flare energies to ≲1043−1049 erg (depending on the distances of the various repeating FRBs, with ≲1039 erg for the Galactic SGR 1935+2154). These limits are generally at least an order of magnitude larger than those inferred from the FRBs themselves, although in the case of FRB 20220912A our simultaneous and rapid follow-up observations severely restrict the model parameter space. We conclude by exploring the potential of future simultaneous and rapid-response observations with large optical telescopes.

Deep Learning Detection and Classification of Gravitational Waves from Neutron Star-Black Hole Mergers
Richard Qiu, Plamen Krastev, Kiranjyot Gill, Edo Berger
Physics Letters B, 2023, Volume 840 [ arXiv:2210.15888 ]

Abstract The Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo Interferometer Collaborations have now detected all three classes of compact binary mergers: binary black hole (BBH), binary neutron star (BNS), and neutron star-black hole (NSBH). For coalescences involving neutron stars, the simultaneous observation of gravitational and electromagnetic radiation produced by an event, has broader potential to enhance our understanding of these events, and also to probe the equation of state (EOS) of dense matter. However, electromagnetic follow-up to gravitational wave (GW) events requires rapid real-time detection and classification of GW signals, and conventional detection approaches are computationally prohibitive for the anticipated rate of detection of next-generation GW detectors. In this work, we present the first deep learning based results of classification of GW signals from NSBH mergers in extit{real} LIGO data. We show for the first time that a deep neural network can successfully distinguish all three classes of compact binary mergers and separate them from detector noise. Specifically, we train a convolutional neural network (CNN) on ∼500,000 data samples of real LIGO noise with injected BBH, BNS, and NSBH GW signals, and we show that our network has high sensitivity and accuracy. Most importantly, we successfully recover the two confirmed NSBH events to-date (GW200105 and GW200115) and the two confirmed BNS mergers to-date (GW170817 and GW190425), together with ≈90% of all BBH candidate events from the third Gravitational Wave Transient Catalog, GWTC-3. These results are an important step towards low-latency real-time GW detection, enabling multi-messenger astronomy.

Identifying Tidal Disruption Events with an Expansion of the FLEET Machine Learning Algorithm
Sebastian Gomez, V. Ashley Villar, Edo Berger, Suvi Gezari, Sjoert van Velzen, Matt Nicholl, Peter K. Blanchard, Kate. D. Alexander
The Astrophysical Journal, 2023, Volume 949, Issue 113 [ arXiv:2210.10810 ]

Abstract We present an expansion of FLEET, a machine learning algorithm optimized to select transients that are most likely to be tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on the light curves and host galaxy information of 4,779 spectroscopically classified transients. For transients with a probability of being a TDE, \ptde>0.5, we can successfully recover TDEs with a ≈40\% completeness and a ≈30\% purity when using the first 20 days of photometry, or a similar completeness and ≈50\% purity when including 40 days of photometry. We find that the most relevant features for differentiating TDEs from other transients are the normalized host separation, and the light curve (g−r) color during peak. Additionally, we use FLEET to produce a list of the 39 most likely TDE candidates discovered by the Zwicky Transient Facility that remain currently unclassified. We explore the use of FLEET for the Legacy Survey of Space and Time on the Vera C. Rubin Observatory (\textit{Rubin}) and the \textit{Nancy Grace Roman Space Telescope} (\textit{Roman}). We simulate the \textit{Rubin} and \textit{Roman} survey strategies and estimate that ∼104 TDEs could be discovered every year by \textit{Rubin}, and ∼200 TDEs per year by \textit{Roman}. Finally, we run FLEET on the TDEs in our \textit{Rubin} survey simulation and find that we can recover ∼30\% of those at a redshift z<0.5 with \ptde>0.5. This translates to ∼3,000 TDEs per year that FLEET could uncover from \textit{Rubin}. FLEET is provided as a open source package on GitHub this https URL

The First Two Years of FLEET: an Active Search for Superluminous Supernovae
Sebastian Gomez, Edo Berger, Peter K. Blanchard, Griffin Hosseinzadeh, Matt Nicholl, Daichi Hiramatsu, V. Ashley Villar, Yao Yin
The Astrophysical Journal, 2023, Volume 949, Issue 114 [ arXiv:2210.10811 | code ]

Abstract In November 2019 we began operating FLEET (Finding Luminous and Exotic Extragalactic Transients), a machine learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Using FLEET, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between November 2019 and January 2022. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50\% for transients with a probability of being a SLSN, \pslsn>0.5; the true on-sky purity we obtained is closer to 80\%. Similarly, we anticipated FLEET could reach a completeness of about 30\%, and we indeed measure an upper limit on the completeness of ≈33\%. Here, we present FLEET 2.0, an updated version of FLEET trained on 4,780 transients (almost 3 times more than in FLEET 1.0). FLEET 2.0 has a similar predicted purity to FLEET 1.0, but outperforms FLEET 1.0 in terms of completeness, which is now closer to ≈40\% for transients with \pslsn>0.5. Additionally, we explore possible systematics that might arise from the use of FLEET for target selection. We find that the population of SLSNe recovered by FLEET is mostly indistinguishable from the overall SLSN population, in terms of physical and most observational parameters. We provide FLEET as an open source package on GitHub this https URL

Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
Gemma Zhang, Siddharth Mishra-Sharma, Cora Dvorkin
Monthly Notices of the Royal Astronomical Society, 2022, Volume 517, Issue 3 [ arXiv:2208.13796 | code ]

Abstract Strong gravitational lensing has emerged as a promising approach for probing dark matter models on sub-galactic scales. Recent work has proposed the subhalo effective density slope as a more reliable observable than the commonly used subhalo mass function. The subhalo effective density slope is a measurement independent of assumptions about the underlying density profile and can be inferred for individual subhalos through traditional sampling methods. To go beyond individual subhalo measurements, we leverage recent advances in machine learning and introduce a neural likelihood-ratio estimator to infer an effective density slope for populations of subhalos. We demonstrate that our method is capable of harnessing the statistical power of multiple subhalos (within and across multiple images) to distinguish between characteristics of different subhalo populations. The computational efficiency warranted by the neural likelihood-ratio estimator over traditional sampling enables statistical studies of dark matter perturbers and is particularly useful as we expect an influx of strong lensing systems from upcoming surveys.

Uncovering dark matter density profiles in dwarf galaxies with graph neural networks
Tri Nguyễn, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib
Physical Review D, 202, Volume 107, Issue 4 [ arXiv:2208.12825 | code ]

Abstract Dwarf galaxies are small, dark matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter -- understanding the spatial dark matter distribution in these systems can be used to constrain microphysical dark matter interactions that influence the formation and evolution of structures in our Universe. We introduce a new method that leverages simulation-based inference and graph-based machine learning in order to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. Our approach aims to address some of the limitations of established methods based on dynamical Jeans modeling. We show that this novel method can place stronger constraints on dark matter profiles and, consequently, has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos, such as the core-cusp discrepancy.

Robust Clustering of the Local Milky Way Stellar Kinematic Substructures with Gaia eDR3
Xiaowei Ou, Lina Necib, Anna Frebel
Royal Astronomical Society, 2023, Volume 521, Issue 2 [ arXiv:2208.01056 ]

Abstract We apply the clustering algorithm HDBSCAN on the Gaia early third data release astrometry combined with the Gaia second data release radial velocity measurements of almost 5.5 million stars to identify the local stellar kinematic substructures in the solar neighborhood. Understanding these structures helps build a more complete picture of the formation of the Milky Way, as well as an empirical phase space distribution of dark matter that would inform detection experiments. The main goal of this study is to provide a list of the most stable clusters, by taking into account the measurement uncertainties and studying the stability of the clustering results. We apply the clustering algorithm in two spaces, in velocity space in order to study recently accreted structures, and in action-angle space to find phase-mixed structures. We find 23 (6) robust clusters in velocity space (action-angle space) that are consistently not associated with noise. They are attributed to the known structures: the Gaia Sausage-Enceladus, the Helmi Stream, and globular cluster NGC 3201 are found in both spaces, while NGC 104 and the thick disk (Sequoia) are identified in velocity space (action-angle space). We discuss the kinematic properties of these structures and study whether many of the small clusters belong to a similar larger cluster based on their chemical abundances. Although we do not identify any new structures, we find that the HDBSCAN member selection of already known structures is unstable to input kinematics of the stars when resampled within their uncertainties. We therefore present the most stable subset of local kinematic structures, which are consistently identified by the clustering algorithm, and emphasize the need to take into account error propagation during both the manual and automated identification of stellar structures, both for existing ones as well as future discoveries. (abridged)

Characterizing the Expected Behavior of Non-Poissonian Template Fitting
Luis Gabriel C. Bariuan, Tracy R. Slatyer
Physical Review D, 2023, Volume 107, Issue 10–15 [ arXiv:2207.13097 ]

Abstract We have performed a systematic study of the statistical behavior of non-Poissonian template fitting (NPTF), a method designed to analyze and characterize unresolved point sources in general counts datasets. In this paper, we focus on the properties and characteristics of the Fermi-LAT gamma-ray data set. In particular, we have simulated and analyzed gamma-ray sky maps under varying conditions of exposure, angular resolution, pixel size, energy window, event selection, and source brightness. We describe how these conditions affect the sensitivity of NPTF to the presence of point sources, for inner-galaxy studies of point sources within the Galactic Center excess, and for the simplified case of isotropic emission. We do not find opportunities for major gains in sensitivity from varying these choices, within the range available with current Fermi-LAT data. We provide an analytic estimate of the NPTF sensitivity to point sources for the case of isotropic emission and perfect angular resolution, and find good agreement with our numerical results for that case.

Reconstructing Cosmological Initial Conditions from Late-Time Structure with Convolutional Neural Networks
Christopher J. Shallue, Daniel J. Eisenstein
Monthly Notices of the Royal Astronomical Society, 2023, Volume 520, Issue 4 [ arXiv:2207.12511 | code ]

Abstract We present a method to reconstruct the initial linear-regime matter density field from the late-time non-linearly evolved density field in which we channel the output of standard first-order reconstruction to a convolutional neural network (CNN). Our method shows dramatic improvement over the reconstruction of either component alone. We show why CNNs are not well-suited for reconstructing the initial density directly from the late-time density: CNNs are local models, but the relationship between initial and late-time density is not local. Our method leverages standard reconstruction as a preprocessing step, which inverts bulk gravitational flows sourced over very large scales, transforming the residual reconstruction problem from long-range to local and making it ideally suited for a CNN. We develop additional techniques to account for redshift distortions, which warp the density fields measured by galaxy surveys. Our method improves the range of scales of high-fidelity reconstruction by a factor of 2 in wavenumber above standard reconstruction, corresponding to a factor of 8 increase in the number of well-reconstructed modes. In addition, our method almost completely eliminates the anisotropy caused by redshift distortions. As galaxy surveys continue to map the Universe in increasingly greater detail, our results demonstrate the opportunity offered by CNNs to untangle the non-linear clustering at intermediate scales more accurately than ever before.

Modeling early-universe energy injection with Dense Neural Networks
Yitian Sun, Tracy R. Slatyer
Physical Review D, Volume 107, Article 063541 [ arXiv:2207.06425 | code ]

Abstract We show that Dense Neural Networks can be used to accurately model the cooling of high-energy particles in the early universe, in the context of the public code package DarkHistory. DarkHistory self-consistently computes the temperature and ionization history of the early universe in the presence of exotic energy injections, such as might arise from the annihilation or decay of dark matter. The original version of DarkHistory uses large pre-computed transfer function tables to evolve photon and electron spectra in redshift steps, which require a significant amount of memory and storage space. We present a light version of DarkHistory that makes use of simple Dense Neural Networks to store and interpolate the transfer functions, which performs well on small computers without heavy memory or storage usage. This method anticipates future expansion with additional parametric dependence in the transfer functions without requiring exponentially larger data tables..

Radio excess from stimulated dark matter decay
Andrea Caputo,Hongwan Liu, Siddharth Mishra-Sharma, Maxim Pospelov, Joshua T. Ruderman
Physical Review D, 2023, Volume 107, Issue 12, 2023 [ arXiv:2206.07713 ]

Abstract Despite an intense theoretical and experimental effort over the past decade, observations of the extragalactic radio background at multiple frequencies below 10 GHz are not understood in terms of known radio sources and may represent a sign of new physics. In this paper, we identify a new class of dark sector models with feebly interacting particles, where dark photons oscillate into ordinary photons that contribute to the radio background. Our scenario can explain both the magnitude and the spectral index of the radio background, while being consistent with other cosmological and astrophysical constraints. These models predict new relativistic degrees of freedom and spectral distortions of the cosmic microwave background, which could be detected in the next generation of experiments.

Strong Lensing Source Reconstruction Using Continuous Neural Fields
Siddharth Mishra-Sharma, Ge Yang
[ arXiv:2206.14820 | code ]

Abstract From the nature of dark matter to the rate of expansion of our Universe, observations of distant galaxies distorted through strong gravitational lensing have the potential to answer some of the major open questions in astrophysics. Modeling galaxy-galaxy strong lensing observations presents a number of challenges as the exact configuration of both the background source and foreground lens galaxy is unknown. A timely call, prompted by a number of upcoming surveys anticipating high-resolution lensing images, demands methods that can efficiently model lenses at their full complexity. In this work, we introduce a method that uses continuous neural fields to non-parametrically reconstruct the complex morphology of a source galaxy while simultaneously inferring a distribution over foreground lens galaxy configurations. We demonstrate the efficacy of our method through experiments on simulated data targeting high-resolution lensing images similar to those anticipated in near-future astrophysical surveys.

The Dark Energy Camera Plane Survey 2 (DECaPS2): More Sky, Less Bias, and Better Uncertainties
A. K. Saydjari, E. F. Schlafly, D. Lang, A. M. Meisner, G. M. Green, C. Zucker, I. Zelko, J. S. Speagle, T. Daylan, A. Lee, F. Valdes, D. Schlegel, D. P. Finkbeiner
The Astrophysical Journal Supplement Series, 2023, Vol 264, Number 2 [ arXiv:2206.11909 | code ]

Abstract Deep optical and near-infrared imaging of the entire Galactic plane is essential for understanding our Galaxy's stars, gas, and dust. The second data release of the DECam Plane Survey (DECaPS2) extends the five-band optical and near-infrared survey of the southern Galactic plane to cover 6.5% of the sky, |b| < 10° and 6° > l > -124°, complementary to coverage by Pan-STARRS1. Typical single-exposure effective depths, including crowding effects and other complications, are 23.5, 22.6, 22.1, 21.6, and 20.8 mag in g, r, i, z, and Y bands, respectively, with around 1 arcsecond seeing. The survey comprises 3.32 billion objects built from 34 billion detections in 21.4 thousand exposures, totaling 260 hours open shutter time on the Dark Energy Camera (DECam) at Cerro Tololo. The data reduction pipeline features several improvements, including the addition of synthetic source injection tests to validate photometric solutions across the entire survey footprint. A convenient functional form for the detection bias in the faint limit was derived and leveraged to characterize the photometric pipeline performance. A new post-processing technique was applied to every detection to de-bias and improve uncertainty estimates of the flux in the presence of structured backgrounds, specifically targeting nebulosity. The images and source catalogs are publicly available at this http URL: http://decaps.skymaps.info/

Revealing the Milky Way’s Most Recent Major Merger with a Gaia EDR3 Catalog of Machine-Learned Line-of-Sight Velocities
Adriana Dropulic, Hongwan Liu, Bryan Ostdiek, Mariangela Lisanti
Monthly Notices of the Royal Astronomical Society, May 2023, Volume 521, Issue 2 [ arXiv:2205.12278 ]

Abstract Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight velocities and associated uncertainties for ~92 million stars. The network, which takes as input a star's parallax, angular coordinates, and proper motions, is trained and validated on ~6.4 million stars in Gaia with complete phase-space information. The network's uncertainty on its velocity prediction is a key aspect of its design; by properly convolving these uncertainties with the inferred velocities, we obtain accurate stellar kinematic distributions. As a first science application, we use the new network-completed catalog to identify candidate stars that belong to the Milky Way's most recent major merger, Gaia-Sausage-Enceladus (GSE). We present the kinematic, energy, angular momentum, and spatial distributions of the ~450,000 GSE candidates in this sample, and also study the chemical abundances of those with cross matches to GALAH and APOGEE. The network's predictive power will only continue to improve with future Gaia data releases as the training set of stars with complete phase-space information grows. This work provides a first demonstration of how to use machine learning to exploit high-dimensional correlations on data to infer line-of-sight velocities, and offers a template for how to train, validate and apply such a neural network when complete observational data is not available.

Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms
Georgios Valogiannis, Cora Dvorkin
Physical Review D, 2022, Volume 106, Article 103509 [ arXiv:2204.13717 ]

Abstract We perform the first application of the wavelet scattering transform (WST) on actual galaxy observations, through a WST analysis of the BOSS DR12 CMASS dataset. We lay out the detailed procedure on how to capture all necessary layers of realism for an application on data obtained from a spectroscopic survey, including the effects of redshift-space anisotropy, non-trivial survey geometry, the shortcomings of the dataset through a set of systematic weights and the Alcock-Paczynski distortion effect. In order to capture the cosmological dependence of the WST, we use galaxy mocks obtained from the state-of-the-art ABACUSSUMMIT simulations, tuned to match the anisotropic correlation function of the BOSS CMASS sample in the redshift range 0.46<z<0.60. Using our theory model for the WST coefficients, as well as for the first 2 multipoles of the galaxy power spectrum, that we use as reference, we perform a likelihood analysis of the CMASS data and obtain the posterior probability distributions of 4 cosmological parameters, {ωb,ωc,ns,σ8}, as well as the Hubble constant, derived from a fixed value of the angular size of the sound horizon at last scattering measured by the Planck satellite, all of which are marginalized over the 7 nuisance parameters of the Halo Occupation Distribution model. The WST is found to deliver a substantial improvement in the values of the predicted 1σ errors compared to the regular power spectrum, which are tighter by a factor in the range 3−6 in the case of flat and uninformative priors and by a factor of 4−28, when a Big Bang Nucleosynthesis prior is applied on the value of ωb. Furthermore, in the latter case, we obtain a 0.6% measurement of the Hubble constant. Our results are investigative and subject to certain approximations in our analysis, that we discuss in the text.

Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification
Brian Hsu, Griffin Hosseinzadeh, V. Ashley Villar, Edo Berger
The Astrophysical Journal, 2022, Volume 937, Number 1 [ arXiv:2204.09809 ]

Abstract With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only ∼0.1% of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS) classified photometrically with our SuperRAENN and Superphot algorithms. We first construct a sub-sample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multi-band light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are overall consistent, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.

Luminous Supernovae: Unveiling a Population Between Superluminous and Normal Core-collapse Supernovae
Sebastian Gomez, Edo Berger, Matt Nicholl, Peter K. Blanchard, Griffin Hosseinzadeh
The Astrophysical Journal, 2022, Volume 941, Number 2 [ arXiv:2204.08486 ]

Abstract Stripped-envelope core-collapse supernovae can be divided into two broad classes: the common Type Ib/c supernovae (SNe Ib/c), powered by the radioactive decay of 56Ni, and the rare superluminous supernovae (SLSNe), most likely powered by the spin-down of a magnetar central engine. Up to now, the intermediate regime between these two populations has remained mostly unexplored. Here, we present a comprehensive study of 40 extit{luminous supernovae} (LSNe), SNe with peak magnitudes of Mr=−19 to −20 mag, bound by SLSNe on the bright end and by SNe Ib/c on the dim end. Spectroscopically, LSNe appear to form a continuum between Type Ic SNe and SLSNe. Given their intermediate nature, we model the light curves of all LSNe using a combined magnetar plus radioactive decay model and find that they are indeed intermediate, not only in terms of their peak luminosity and spectra, but also in their rise times, power sources, and physical parameters. We sub-classify LSNe into distinct groups that are either as fast-evolving as SNe Ib/c or as slow-evolving as SLSNe, and appear to be either radioactively or magnetar powered, respectively. Our findings indicate that LSNe are powered by either an over-abundant production of 56Ni or by weak magnetar engines, and may serve as the missing link between the two populations.

Quantification of high dimensional non-Gaussianities and its implication to Fisher analysis in cosmology
Core Francisco Park, Erwan Allys, Francisco Villaescusa-Navarro, Douglas P. Finkbeiner
The Astrophysical Journal, Volume 946, Number 2 [ arXiv:2204.05435 | code ]

Abstract It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been proposed to extract information from non-Gaussian cosmological fields that perform better than the power spectrum. The Fisher matrix formalism is commonly used to quantify the accuracy with which a given statistic can constrain the value of the cosmological parameters. However, these calculations typically rely on the assumption that the likelihood of the considered statistic follows a multivariate Gaussian distribution. In this work we follow Sellentin & Heavens (2017) and use two different statistical tests to identify non-Gaussianities in different statistics such as the power spectrum, bispectrum, marked power spectrum, and wavelet scatering transform (WST). We remove the non-Gaussian components of the different statistics and perform Fisher matrix calculations with the extit{Gaussianized} statistics using Quijote simulations. We show that constraints on the parameters can change by a factor of ∼2 in some cases. We show with simple examples how statistics that do not follow a multivariate Gaussian distribution can achieve artificially tight bounds on the cosmological parameters when using the Fisher matrix formalism. We think that the non-Gaussian tests used in this work represent a powerful tool to quantify the robustness of Fisher matrix calculations and their underlying assumptions. We release the code used to compute the power spectra, bispectra, and WST that can be run on both CPUs and GPUs.

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials
Andrew Ma, Yang Zhang, Thomas Christensen, Hoi Chun Po, Li Jing, Liang Fu, Marin Soljačić
American Chemical Society Publications [ arXiv:2202.05255 | code ]

Abstract Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently-known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wavefunction. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput strategy for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation.

Constraining the Time of Gravitational Wave Emission from Core-Collapse Supernovae
Kiranjyot Gill, Griffin Hosseinzadeh, Edo Berger, Michele Zanolin, Marek Szczepanczyk
The Astrophysical Journal, 2022, Volume 931, Number 2 [ arXiv:2201.03609 ]

Abstract The advent of sensitive gravitational wave (GW) detectors, coupled with wide-field, high cadence optical time-domain surveys, raises the possibility of the first join GW-electromagnetic (EM) detections of core-collapse supernovae (CCSNe). For targeted searches of Gas from CCSNe optical observation can be used to increase the sensitivity of the search by restricting the relevant time interval, defined here as the GW search window (GSW). The extent of the GSW is a critical factor in determining the achievable false alarm probability (FAP) for a triggered CCSN search. The ability to constrain the GSW from optical observations depends on how early a CCSN is detected, as well as the ability to model the early optical emission. Here we present several approaches to constrain the GSW, ranging in complexity from model-independent analytical fits of the early light curve, model-dependent fits of the rising or entire light curve, and a new data-driven approach using existing well-sampled CCSN light curves from {\it Kepler} and the Transiting Exoplanet Survey Satellite (TESS). We use these approaches to determine the time of core-collapse and its associated uncertainty (i.e., the GSW). We apply our methods to two Type II See that occurred during LIGO/Virgo Observing Run 3: SN\,2019fcn and SN\,2019ejj (both in the same galaxy at d = 15.7 Mac). Our approach shortens the duration of the GSW and improves the robustness of the GSW compared to techniques used in past GW CCSN searches.

Photometry on Structured Backgrounds: Local Pixelwise Infilling by Regression
Andrew K. Saydjari, Douglas P. Finkbeiner
The Astrophysical Journal, 2022, Volume 933, Number 2 [ arXiv:2201.07246 | code ]

Abstract Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixelwise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty on that prediction in order to improve estimates of flux and flux uncertainty. We show the validity of our model on synthetic data and real dust fields. We further demonstrate that the method is stable even in the crowded field limit. While we focus on optical-IR photometry, this method is not restricted to those wavelengths. We apply this technique to the 34 billion detections in the second data release of the Dark Energy Camera Plane Survey (DECaPS2). In addition to removing many >3σ outliers and improving uncertainty estimates by a factor of ∼2−3 on nebulous fields, we also show that our method is well-behaved on uncrowded fields. The entirely post-processing nature of our implementation of LPI photometry allows it to easily improve the flux and flux uncertainty estimates of past as well as future surveys.

Impact of Massive Binary Star and Cosmic Evolution on Gravitational Wave Observations II: Double Compact Object Rates and Properties
Floor S. Broekgaarden, Edo Berger, Simon Stevenson, Stephen Justham, Ilya Mandel, Martyna Churślińska, Like A. C. van Son, Tom Wagg, Alejandro Vigna-Gómez, Selma E. De Mink, Debatri Chattopadhyay, Coenraad J. Neijssel
Monthly Notices of the Royal Astronomical Society, 2022, Volume 516, Issue 4, Pages 5737–5761 [ arXiv:2112.05763 | code ]

Abstract Making the most of the rapidly increasing population of gravitational-wave detections of black hole (BH) and neutron star (NS) mergers requires comparing observations with population synthesis predictions. In this work we investigate the combined impact from the key uncertainties in population synthesis modelling of the isolated binary evolution channel: the physical processes in massive binary-star evolution and the star formation history as a function of metallicity, Z, and redshift z,S(Z,z). Considering these uncertainties we create 560 different publicly available model realizations and calculate the rate and distribution characteristics of detectable BHBH, BHNS, and NSNS mergers. We find that our stellar evolution and S(Z,z) variations can impact the predicted intrinsic and detectable merger rates by factors 102-104. We find that BHBH rates are dominantly impacted by S(Z,z) variations, NSNS rates by stellar evolution variations and BHNS rates by both. We then consider the combined impact from all uncertainties considered in this work on the detectable mass distribution shapes (chirp mass, individual masses and mass ratio). We find that the BHNS mass distributions are predominantly impacted by massive binary-star evolution changes. For BHBH and NSNS we find that both uncertainties are important. We also find that the shape of the delay time and birth metallicity distributions are typically dominated by the choice of S(Z,z) for BHBH, BHNS and NSNS. We identify several examples of robust features in the mass distributions predicted by all 560 models, such that we expect more than 95% of BHBH detections to contain a BH ≳8M⊙ and have mass ratios ≲4. Our work demonstrates that it is essential to consider a wide range of allowed models to study double compact object merger rates and properties.

Substructure Detection Reanalyzed: Dark Perturber shown to be a Line-of-Sight Halo
Atınç Çağan Şengül, Cora Dvorkin, Bryan Ostdiek, Arthur Tsang
Monthly Notices of the Royal Astronomical Society, 2022, Volume 515, Issue 3, Pages 4391–4401 [ arXiv:2112.00749 | code ]

Abstract Observations of structure at sub-galactic scales are crucial for probing the properties of dark matter, which is the dominant source of gravity in the universe. It will become increasingly important for future surveys to distinguish between line-of-sight halos and subhalos to avoid wrong inferences on the nature of dark matter. We reanalyze a sub-galactic structure (in lens JVAS B1938+666) that has been previously found using the gravitational imaging technique in galaxy-galaxy lensing systems. This structure has been assumed to be a satellite in the halo of the main lens galaxy. We fit the redshift of the perturber of the system as a free parameter, using the multi-plane thin-lens approximation, and find that the redshift of the perturber is zint=1.22+0.11−0.11 (with a main lens redshift of z=0.881). Our analysis indicates that this structure is more massive than the previous result by more than an order of magnitude. This constitutes the first dark perturber shown to be a line-of-sight halo with a gravitational lensing method.

New limits on the light dark matter: proton cross section from the cosmic large-scale structure
Keir K. Rogers, Cora Dvorkin, Hiranya V. Peiris
Physical Review Letters, 2022, Volume 128, Article 171301 [ arXiv:2111.10386 ]

Abstract We set the strongest limits to-date on the velocity-independent dark matter (DM) - proton cross section σ for DM masses m=10keV to 100GeV, using large-scale structure traced by the Lyman-alpha forest: e.g., a 95% lower limit σ<6×10−30cm2, for m=100keV. Our results complement direct detection, which has limited sensitivity to sub-GeV DM. We use an emulator of cosmological simulations, combined with data from the smallest cosmological scales used to-date, to model and search for the imprint of primordial DM-proton collisions. Cosmological bounds are improved by up to a factor of 25.

A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess
Siddharth Mishra-Sharma, Kyle Cranmer
Physical Review D, 2922, Volume 105, Article 063017 [ arXiv:2110.06931 | code ]

Abstract The nature of the Fermi gamma-ray Galactic Center Excess (GCE) has remained a persistent mystery for over a decade. Although the excess is broadly compatible with emission expected due to dark matter annihilation, an explanation in terms of a population of unresolved astrophysical point sources e.g., millisecond pulsars, remains viable. The effort to uncover the origin of the GCE is hampered in particular by an incomplete understanding of diffuse emission of Galactic origin. This can lead to spurious features that make it difficult to robustly differentiate smooth emission, as expected for a dark matter origin, from more "clumpy" emission expected for a population of relatively bright, unresolved point sources. We use recent advancements in the field of simulation-based inference, in particular density estimation techniques using normalizing flows, in order to characterize the contribution of modeled components, including unresolved point source populations, to the GCE. Compared to traditional techniques based on the statistical distribution of photon counts, our machine learning-based method is able to utilize more of the information contained in a given model of the Galactic Center emission, and in particular can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the gamma-ray map. This makes the method demonstrably more resilient to certain forms of model misspecification. On application to Fermi data, the method generically attributes a smaller fraction of the GCE flux to unresolved point sources when compared to traditional approaches. We nevertheless infer such a contribution to make up a non-negligible fraction of the GCE across all analysis variations considered, with at least 38+9−19% of the excess attributed to unresolved points sources in our baseline analysis.

Inferring dark matter substructure with astrometric lensing beyond the power spectrum
Siddharth Mishra-Sharma
[ arXiv:2110.01620 | code ]

Abstract Astrometry -- the precise measurement of positions and motions of celestial objects -- has emerged as a promising avenue for characterizing the dark matter population in our Galaxy. By leveraging recent advances in simulation-based inference and neural network architectures, we introduce a novel method to search for global dark matter-induced gravitational lensing signatures in astrometric datasets. Our method based on neural likelihood-ratio estimation shows significantly enhanced sensitivity to a cold dark matter population and more favorable scaling with measurement noise compared to existing approaches based on two-point correlation statistics, establishing machine learning as a powerful tool for characterizing dark matter using astrometric data.

Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris, Erik Katsavounidis, Steven Timm, Burt Holzman
[ arXiv:2108.12430 | code ]

Abstract The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform
Georgios Valogiannis, Cora Dvorkin
Physical Review D, 2022, 105, 103534 [ arXiv:2108.07821 ]

Abstract Optimal extraction of the non-Gaussian information encoded in the Large-Scale Structure (LSS) of the universe lies at the forefront of modern precision cosmology. We propose achieving this task through the use of the Wavelet Scattering Transform (WST), which subjects an input field to a layer of non-linear transformations that are sensitive to non-Gaussianity in spatial density distributions through a generated set of WST coefficients. In order to assess its applicability in the context of LSS surveys, we apply the WST on the 3D overdensity field obtained by the Quijote simulations, out of which we extract the Fisher information in 6 cosmological parameters. It is subsequently found to deliver a large improvement in the marginalized errors on all parameters, ranging between 1.2−4× tighter than the corresponding ones obtained from the regular 3D cold dark matter + baryon power spectrum, as well as a 50% improvement over the neutrino mass constraint given by the marked power spectrum. Through this first application on 3D cosmological fields, we demonstrate the great promise held by this novel statistic and set the stage for its future application to actual galaxy observations.

A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients
Ashley Villar, Miles Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
The Astrophysical Journal Supplement Series, Volume 255 [ | code ]

Abstract The Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo Interferometer Collaborations have now detected all three classes of compact binary mergers: binary black hole (BBH), binary neutron star (BNS), and neutron star-black hole (NSBH). For coalescences involving neutron stars, the simultaneous observation of gravitational and electromagnetic radiation produced by an event, has broader potential to enhance our understanding of these events, and also to probe the equation of state (EOS) of dense matter. However, electromagnetic follow-up to gravitational wave (GW) events requires rapid real-time detection and classification of GW signals, and conventional detection approaches are computationally prohibitive for the anticipated rate of detection of next-generation GW detectors. In this work, we present the first deep learning based results of classification of GW signals from NSBH mergers in extit{real} LIGO data. We show for the first time that a deep neural network can successfully distinguish all three classes of compact binary mergers and separate them from detector noise. Specifically, we train a convolutional neural network (CNN) on ∼500,000 data samples of real LIGO noise with injected BBH, BNS, and NSBH GW signals, and we show that our network has high sensitivity and accuracy. Most importantly, we successfully recover the two confirmed NSBH events to-date (GW200105 and GW200115) and the two confirmed BNS mergers to-date (GW170817 and GW190425), together with ≈90% of all BBH candidate events from the third Gravitational Wave Transient Catalog, GWTC-3. These results are an important step towards low-latency real-time GW detection, enabling multi-messenger astronomy.

A Compound Poisson Generator approach to Point-Source Inference in Astrophysics
Gabriel H. Collin, Nicholas L. Rodd, Tyler Erjavec, Kerstin Perez
The Astrophysical Journal, 2022, Volume 260, Number 2 [ arXiv:2104.04529 | code ]

Abstract The identification and description of point sources is one of the oldest problems in astronomy; yet, even today the correct statistical treatment for point sources remains as one of the field's hardest problems. For dim or crowded sources, likelihood based inference methods are required to estimate the uncertainty on the characteristics of the source population. In this work, a new parametric likelihood is constructed for this problem using Compound Poisson Generator (CPG) functionals which incorporate instrumental effects from first principles. We demonstrate that the CPG approach exhibits a number advantages over Non-Poissonian Template Fitting (NPTF) - an existing parametric likelihood method - in a series of test scenarios in the context of X-ray astronomy. These demonstrations show that the effect of the point-spread function, effective area, and choice of point-source spatial distribution cannot, in general, be factorised as they are in the NPTF construction, while the new CPG construction is validated in these scenarios. Separately, an examination of the diffuse-flux emission limit is used to show that most simple choices of priors on the standard parameterisation of the population model can result in unexpected biases: when a model comprising both a point-source population and diffuse component is applied to this limit, nearly all observed flux will be assigned to either the population or to the diffuse component. A new parametrisation is presented for these priors which is demonstrated to properly estimate the uncertainties in this limit. In this choice of priors, the CPG correctly identifies that the fraction of flux assigned to the population model cannot be constrained by the data.

Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations
Adriana Dropulic, Bryan Ostdiek, Laura J. Chang, Hongwan Liu, Timothy Cohen, and Mariangela Lisanti
The Astrophysical Journal Letters, 2021, 915, L14 [ arXiv:2103.14039 | code ]

Abstract The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. We utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star's 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ∼5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.

The Luminous and Double-Peaked Type Ic Supernova 2019stc: Evidence for Multiple Energy Sources
Sebastian Gomez, Edo Berger, Griffin Hosseinzadeh, Peter K. Blanchard, Matt Nicholl, V. Ashley Villar
The Astrophysical Journal, 2021, Vol. 913, Article 143 [ arXiv:2103.02611 ]

Abstract We present optical photometry and spectroscopy of SN\,2019stc (=ZTF19acbonaa), an unusual Type Ic supernova (SN Ic) at a redshift of z=0.117. SN\,2019stc exhibits a broad double-peaked light curve, with the first peak having an absolute magnitude of Mr=−20.0 mag, and the second peak, about 80 rest-frame days later, Mr=−19.2 mag. The total radiated energy is large, Erad≈2.5×1050 erg. Despite its large luminosity, approaching those of Type I superluminous supernovae (SLSNe), SN\,2019stc exhibits a typical SN Ic spectrum, bridging the gap between SLSNe and SNe Ic. The spectra indicate the presence of Fe-peak elements, but modeling of the first light curve peak with radioactive heating alone leads to an unusually high nickel mass fraction of fNi≈31% (MNi≈3.2 M⊙). Instead, if we model the first peak with a combined magnetar spin-down and radioactive heating model we find a better match with Mej≈4 M⊙, a magnetar spin period of Pspin≈7.2 ms and magnetic field of B≈1014 G, and fNi≲0.2 (consistent with SNe Ic). The prominent second peak cannot be naturally accommodated with radioactive heating or magnetar spin-down, but instead can be explained as circumstellar interaction with ≈0.7 M⊙ of hydrogen-free material located ≈400 AU from the progenitor. Including the remnant mass leads to a CO core mass prior to explosion of ≈6.5 M⊙. The host galaxy has a metallicity of ≈0.26 Z⊙, low for SNe Ic but consistent with SLSNe. Overall, we find that SN\,2019stc is a transition object between normal SNe Ic and SLSNe.

On the convergence of group-sparse autoencoders
Emmanouil Theodosis, Bahareh Tolooshams, Pranay Tankala, Abiy Tasissa, Demba Ba
[ arXiv:2102.07003 ]

Abstract Recent approaches in the theoretical analysis of model-based deep learning architectures have studied the convergence of gradient descent in shallow ReLU networks that arise from generative models whose hidden layers are sparse. Motivated by the success of architectures that impose structured forms of sparsity, we introduce and study a group-sparse autoencoder that accounts for a variety of generative models, and utilizes a group-sparse ReLU activation function to force the non-zero units at a given layer to occur in blocks. For clustering models, inputs that result in the same group of active units belong to the same cluster. We proceed to analyze the gradient dynamics of a shallow instance of the proposed autoencoder, trained with data adhering to a group-sparse generative model. In this setting, we theoretically prove the convergence of the network parameters to a neighborhood of the generating matrix. We validate our model through numerical analysis and highlight the superior performance of networks with a group-sparse ReLU compared to networks that utilize traditional ReLUs, both in sparse coding and in parameter recovery tasks. We also provide real data experiments to corroborate the simulated results, and emphasize the clustering capabilities of structured sparsity models.

Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
Plamen G. Krastev, Kiranjyot Gill, V. Ashley Villar, Edo Berger
Physics Letters B, 2021, Vol. 815, Article 136161 [ arXiv:2012.13101 ]

Abstract One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Phys. Rev. X 9 (2019), 031040]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.

This project is supported by National Science Foundation under Cooperative Agreement PHY-2019786. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.