$\mathrm{H}\mathrm{ALO}\mathrm{F}\mathrm{LOW}$ I: Neural Inference of Halo Mass from Galaxy Photometry and Morphology

Abstract

We present $\mathrm{H}\mathrm{ALO}\mathrm{F}\mathrm{LOW}$, a new machine learning approach for inferring the mass of host dark matter halos, $M_h$, from the photometry and morphology of galaxies. $\mathrm{H}\mathrm{ALO}\mathrm{F}\mathrm{LOW}$ uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-the-art synthetic galaxy images from Bottrell et al. (2023; arXiv:2308.14793) that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program (HSC-SSP) observations. We design $\mathrm{H}\mathrm{ALO}\mathrm{F}\mathrm{LOW}$ to infer $M_h$ and stellar mass, $M_{\ast }$, using $grizy$ band magnitudes, morphological properties quantifying characteristic size, concentration, and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that $\mathrm{H}\mathrm{ALO}\mathrm{F}\mathrm{LOW}$ infers accurate and unbiased posteriors of $M_h$. Furthermore, we quantify the full information content in the photometric observations of galaxies in constraining $M_h$. With magnitudes alone, we infer $M_h$ with $\ddot{I}{ƒ}_{\log M_h}\sim 0.115$ and 0.182 dex for field and group galaxies. Including morphological properties significantly improves the precision of $M_h$ constraints, as does total satellite luminosity: $\ddot{I}{ƒ}_{\log M_h}\sim 0.095$ and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve $M_h$ constraints by $\sim$40\%. In subsequent papers, we will validate and calibrate $\mathrm{H}\mathrm{ALO}\mathrm{F}\mathrm{LOW}$ with galaxy-galaxy lensing measurements on real observational data.

17 pages, 5 figures; submitted to ApJ