HaloFlow. I. Neural Inference of Halo Mass from Galaxy Photometry and Morphology

Abstract

We present HaloFlow , a new machine-learning approach for inferring the mass of host dark matter halos, M _h , from the photometry and morphology of galaxies ( https://github.com/changhoonhahn/haloflow/ ). HaloFlow 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. that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program observations. We design HaloFlow to infer M _h and stellar mass, M _* , using grizy band magnitudes, morphological properties quantifying characteristic size, concentration and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that HaloFlow 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 ${\sigma }_{\mathrm{log}{M}_{h}}\sim 0.115$ 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: ${\sigma }_{\mathrm{log}{M}_{h}}\sim 0.095$ and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve M _h constraints by ∼40%. In subsequent papers, we will validate and calibrate HaloFlow with galaxy–galaxy lensing measurements on real observational data.

Keywords

• Large-scale structure of the universe
• Cosmology
• Galaxy clusters
• Galaxy groups
• Neural networks
• Bayesian statistics