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 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: 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.
- Publication:
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The Astrophysical Journal
- Pub Date:
- June 2024
- DOI:
- 10.3847/1538-4357/ad4344
- arXiv:
- arXiv:2310.04503
- Bibcode:
- 2024ApJ...968...90H
- Keywords:
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- Large-scale structure of the universe;
- Cosmology;
- Galaxy clusters;
- Galaxy groups;
- Neural networks;
- Bayesian statistics;
- 902;
- 343;
- 584;
- 597;
- 1933;
- 1900;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Cosmology and Nongalactic Astrophysics
- E-Print:
- 17 pages, 5 figures