2 papers
astro-ph.GA2026
The Deep Learning Halo Definer: A Multimodal Framework for Halo Mass and Gas Fraction Inference on Galaxy Groups and Clusters
Caleb Ogle, Kalvyn N. Poncelet Adams, Benjamin D. Oppenheimer +5
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…
astro-ph.GA2026
CNN-Based Inference of Gaseous Halo Properties from Synthetic X-ray and 21-cm HI Observations
Kalvyn N. Poncelet Adams, Benjamin D. Oppenheimer, Naomi Gluck +3
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 ne…