CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
Abstract
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.
Related
- The Goldilocks Prior: Physically Consistent Inference of Neutron Star Properties from Gravitational Wave Observations
- The Halo Gas of Local Spiral Galaxies and the Link to Gaseous Satellites
- The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters
- LMC-induced Perturbations in the Milky Way Halo II: Bridging Field-level Inference and Summary-level Simulation-Based Inference
- A Possible Hidden Hot Halo Component Revealed by Joint X-ray and Sunyaev--Zel'dovich Observations
BibTeX
@misc{adams2026cnnbased,
title = {{CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations}},
author = {Kalvyn N. Poncelet Adams and Benjamin D. Oppenheimer and Naomi Gluck and Caleb Ogle and Matthew Ho and Daisuke Nagai},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.16421},
eprint = {2609.16421},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.16421},
}
RIS
TY - UNPB
TI - CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
AU - Kalvyn N. Poncelet Adams
AU - Benjamin D. Oppenheimer
AU - Naomi Gluck
AU - Caleb Ogle
AU - Matthew Ho
AU - Daisuke Nagai
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.16421
UR - https://arxiv.org/abs/2609.16421
AB - 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.
ER -