Inferring the Total Mass of Galaxy Halos Using Photometry
Abstract
Galaxy size, as defined as the radius at which the stellar surface density reaches 1 M⊙ pc−2 (R1), has been shown to correlate more tightly with stellar mass than the effective radius. Cosmological simulations further predict a tight relation between R1 and dark matter halo mass (M200), but this prediction has not yet been tested observationally. We present the first observational test of the predicted R1--M200 relation and assess whether galaxy size, defined through R1, can serve as a robust estimator of dark matter halo mass. We analyze galaxies from the SPARC database, combining high-quality rotation curves to infer dark matter halo masses with stellar surface density profiles derived from photometric data to measure R1. We then compare the observed relation with the predictions from state-of-the-art cosmological simulations. We find good agreement between observations and simulations: galaxies follow the same tight R1--M200 relation predicted by numerical models across the explored mass range, with low intrinsic scatter. We provide the first observational validation of the R1--M200 relation, demonstrating that R1 is a simple, physically motivated, and robust estimator of dark matter halo mass. Because R1 is determined entirely from photometric observations, it provides a tool for inferring halo masses from imaging data alone, with applications to studies of galaxy formation and evolution.
Related
- A Systematic Assessment of Total Stellar Mass Measurements with Spatially Resolved Photometry and Medium Bands at 1 < z < 9: The Impact of Mass-to-Light Variations and Outshining
- Radio Halos in Galaxy Clusters as unveiled by the SKA telescope
- Solitary dwarf galaxy groups as tracers of dark matter halos in the local Universe
- Massive Galaxy Halos Contain Less Inner Dark Matter Than Predicted
- SpecLess: A Multi Survey Python Package for Machine Learning Classification of Galaxy Morphology and Activity from Photometry
BibTeX
@misc{brook2026inferring,
title = {{Inferring the Total Mass of Galaxy Halos Using Photometry}},
author = {Chris Brook and Arianna di Cintio and Claudio Dalla Vecchia and Salvador Cardona Barrero and Elena Arjona-Gálvez},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35366},
eprint = {2610.35366},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35366},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Inferring the Total Mass of Galaxy Halos Using Photometry
AU - Chris Brook
AU - Arianna di Cintio
AU - Claudio Dalla Vecchia
AU - Salvador Cardona Barrero
AU - Elena Arjona-Gálvez
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.35366
UR - https://arxiv.org/abs/2610.35366
AB - Galaxy size, as defined as the radius at which the stellar surface density reaches 1 M_⊙ pc^−2 (R_1), has been shown to correlate more tightly with stellar mass than the effective radius. Cosmological simulations further predict a tight relation between R_1 and dark matter halo mass (M_200), but this prediction has not yet been tested observationally. We present the first observational test of the predicted R_1--M_200 relation and assess whether galaxy size, defined through R_1, can serve as a robust estimator of dark matter halo mass. We analyze galaxies from the SPARC database, combining high-quality rotation curves to infer dark matter halo masses with stellar surface density profiles derived from photometric data to measure R_1. We then compare the observed relation with the predictions from state-of-the-art cosmological simulations. We find good agreement between observations and simulations: galaxies follow the same tight R_1--M_200 relation predicted by numerical models across the explored mass range, with low intrinsic scatter. We provide the first observational validation of the R_1--M_200 relation, demonstrating that R_1 is a simple, physically motivated, and robust estimator of dark matter halo mass. Because R_1 is determined entirely from photometric observations, it provides a tool for inferring halo masses from imaging data alone, with applications to studies of galaxy formation and evolution.
ER -