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

Abstract

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.