arXiv subjects

Naomi Gluck

Publications and source records attributed to Naomi Gluck.

5 recordsLinked to original sources

arXiv2026

The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters

Accurately inferring dark matter halo properties like the total halo mass (M_{halo}) and gas fractions (f_{gas}) remains particularly challenging at group scales, where low member counts, shallow potential wells, and AGN feedback-driven baryon expulsion introduce significant observational scatter. As large-scale surveys begin to provide unprecedented multi-wavelength data, there is a pressing need for methods that can jointly leverage diverse observables to overcome these uncertainties. We introduce the Deep Learning Halo Definer (DLHD), a multimodal deep learning framework that simultaneously processes galaxy catalogues and X-ray imaging through the combination of a Deep Sets and a Convolutional Neural Network (CNN) to improve M_{halo} and f_{gas} estimation for galaxy groups and clusters. Using mock datasets derived from the IllustrisTNG300 hydrodynamic simulation, we demonstrate that the DLHD outperforms each of its component networks individually, achieving RMSE improvements in M_{halo} of 1.9x over Deep Sets and 1.3x over the CNN alone. For the gas fraction enclosed within R_{200c}, DLHD reduces RMSE by 2.0x relative to Deep Sets and 1.1x relative to the CNN alone, with consistent improvements across other apertures. These results highlight a novel ability to leverage multi-band information inaccessible to single-modality methods, positioning DLHD as a promising tool for next-generation survey analyses.

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arXiv2026

CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations

Quantifying the information content in multi-wavelength observations is critical for setting exposure times for upcoming X-ray and 21-cm HI radio surveys. We train convolutional neural networks (CNNs) on mock observations of halos from the IllustrisTNG100 and TNG300 simulations, combining data from soft X-ray channels from a CCD or a microcalorimeter with HI intensity, velocity, and dispersion maps, to infer halo mass, gas fractions, metallicity, and [O/Fe] abundance. Multi-band (X-ray and HI) combinations consistently outperform single-band inference for gas fractions. X-ray outperforms HI observations for measuring halo mass, but both bands contribute similarly when measuring the cool (T<10^5 K) gas fraction in halos with significant cool gas content. Using matched exposure times, a micro-calorimeter improves metallicity inference over the CCD by a factor of 1.75, enabling precise measurements of [O/Fe] alpha-enhancement for the largest halos. The larger volume of TNG300 allows inference of group halo masses, finding an inference RMSE of 0.04 dex with a 100 ksec X-ray exposure time. These results demonstrate how deep learning can evaluate strategies for developing instruments and designing surveys for these expensive observations targeting gaseous halos.

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arXiv2026

Benchmarking Machine Learning Emulators of Stellar Evolution for Precision Asteroseismology

Fast and accurate stellar evolution emulators---surrogate models that approximate expensive simulation outputs with machine learning (ML)---are powerful tools for modern stellar characterization, hierarchical inference, and population synthesis. We analyze the grid density required for reliable emulation by training ML algorithms on main-sequence models with masses M=[0.7,1.2] solar masses. This range is challenging to emulate due to rapidly varying evolutionary behavior caused by the radiative-to-convective core transition, as well as the requirement to match the part-per-thousand seismic precision that has been delivered for such stars from the NASA Kepler mission. Generating grids from analytical models, as well as MESA, YREC, MIST, and ASTEC, we compare linear interpolation, k-nearest neighbors, random forests, and neural networks (NNs) in interpolating the stellar observables: T_eff, L, Delta nu, and nu_max. While NNs outperform other methods, sparse grids induce localized failures in the core-transition region, resulting in unstable derivatives, ensemble disagreement, and fragmented posterior distributions during inference. Performance gains from denser grids are non-uniform, suggesting that adaptive grid generation should be favored over uniform refinement. Finally, we show that NN ensembles allow for localized uncertainty propagation, more accurately reflecting emulator reliability across parameter space than global uncertainty estimates. As we consider only the two-dimensional case of varying only stellar mass and age along the main sequence, these results represent a lower bound on the challenge in emulating stellar evolution simulations for precision asteroseismology.

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arXiv2024

An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks

The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique using convolutional neural networks (CNNs) to infer broad-scale physical properties of a galaxy's CGM and its halo mass for the first time. Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft X-ray and 21-cm (HI) radio 2D maps to trace hot and cool gas, respectively, around galaxies, groups, and clusters. Our CNNs offer the unique ability to train and test on ''multifield'' datasets comprised of both HI and X-ray maps, providing complementary information about physical CGM properties and improved inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful enough to infer individual halos with masses $\log(M_{\rm{halo}}/M_{\odot}) < 12.5$. The multifield improves the inference for all halo masses. Generally, the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer CGM properties. Cross-simulation analysis -- training on one galaxy formation model and testing on another -- highlights the challenges of developing CNNs trained on a single model to marginalize over astrophysical uncertainties and perform robust inferences on real data. The next crucial step in improving the resulting inferences on physical CGM properties hinges on our ability to interpret these deep-learning models.

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arXiv2020

Enhanced mass-loss rate evolution of stars with $\gtrsim 18 M_\odot$ and missing optically-observed type II core-collapse supernovae

We evolve stellar models with zero-age main sequence (ZAMS) mass of $M_{\rm ZAMS} \gtrsim 18 M_\odot$ under the assumption that they experience an enhanced mass-loss rate when crossing the instability strip at high luminosities and conclude that most of them end as type Ibc supernovae (SNe Ibc) or dust-obscured SNe II. We explore what level of enhanced mass-loss rate during the instability strip would be necessary to explain the `red supergiant (RSG) problem'. This problem refers to the dearth of observed core-collapse supernovae progenitors with $M_{\rm ZAMS} \gtrsim 18 M_\odot$. Namely, we examine what enhanced mass loss rate could make it possible for all these stars actually to explode as CCSNe. We find that the mass-loss rate should increase by a factor of at least about ten. We reach this conclusion by analyzing the hydrogen mass in the stellar envelope and the optical depth of the dusty wind at the explosion, and crudely estimate that under our assumptions only about a fifth of these stars explode as unobscured SNe II and SNe IIb. About 10-15 per cent end as obscured SNe II that are infrared-bright but visibly very faint, and the rest, about 65-70 per cent, end as SNe Ibc. However, the statistical uncertainties are still too significant to decide whether many stars with $M_{\rm ZAMS} \gtrsim 18M_\odot$ do not explode as expected in the neutrino driven explosion mechanism, or whether all of them explode as CCSNe, as expected by the jittering jets explosion mechanism.

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