Tighter Dark Matter Constraints from the Projected Mass Method: A Neural Network Enhanced Method for Galaxy Groups and Clusters
Abstract
Measuring the total mass of the Milky Way and nearby galaxy groups is difficult because classical dynamical estimators rely on assumptions about satellite orbital geometry that are rarely satisfied in practice, and because only a handful of satellite galaxies are typically available as kinematic tracers. We present a new framework that corrects the well-known Projected Mass Estimator (PME) using a residual neural network trained on thousands of simulated galaxy groups from the IllustrisTNG cosmological simulation. Separate networks are trained for each satellite sample size, from as few as 5 satellites up to 50, so that the correction automatically accounts for the statistical noise that dominates when only a small number of tracers is available. In tests on simulated halos, the classical PME systematically overestimates halo masses by factors of Mproj/Mtrue = 1.30+0.72−0.62 (using the 2D distance) and 1.46+0.97−0.72 (using the 3D distance), with RMSE of 0.29 and 0.32 dex respectively. The neural-network correction reduces this to Mproj/Mtrue = 1.02+0.30−0.26 with an RMSE of 0.13 dex. Applied to the Milky Way, the method yields a total mass of MMW = 1.144+0.399−0.296×1012 M⊙, with estimates based on the brightest 5-10 satellites favoring a somewhat lower range of (0.8−0.95)×1012 M⊙. The modified PME gives a tighter constraint on the virial masses and the inferred dark matter content of galaxy groups and clusters.
Versions
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BibTeX
@misc{huang2026tighter,
title = {{Tighter Dark Matter Constraints from the Projected Mass Method: A Neural Network Enhanced Method for Galaxy Groups and Clusters}},
author = {Yinbo Huang and David Benisty},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.15339},
eprint = {2607.15339},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.15339},
}
RIS
TY - UNPB
TI - Tighter Dark Matter Constraints from the Projected Mass Method: A Neural Network Enhanced Method for Galaxy Groups and Clusters
AU - Yinbo Huang
AU - David Benisty
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2607.15339
UR - https://arxiv.org/abs/2607.15339
AB - Measuring the total mass of the Milky Way and nearby galaxy groups is difficult because classical dynamical estimators rely on assumptions about satellite orbital geometry that are rarely satisfied in practice, and because only a handful of satellite galaxies are typically available as kinematic tracers. We present a new framework that corrects the well-known Projected Mass Estimator (PME) using a residual neural network trained on thousands of simulated galaxy groups from the IllustrisTNG cosmological simulation. Separate networks are trained for each satellite sample size, from as few as 5 satellites up to 50, so that the correction automatically accounts for the statistical noise that dominates when only a small number of tracers is available. In tests on simulated halos, the classical PME systematically overestimates halo masses by factors of M_proj/M_true = 1.30^+0.72_−0.62 (using the 2D distance) and 1.46^+0.97_−0.72 (using the 3D distance), with RMSE of 0.29 and 0.32 dex respectively. The neural-network correction reduces this to M_proj/M_true = 1.02^+0.30_−0.26 with an RMSE of 0.13 dex. Applied to the Milky Way, the method yields a total mass of M_MW = 1.144^+0.399_−0.296×10^12 M_⊙, with estimates based on the brightest 5-10 satellites favoring a somewhat lower range of (0.8−0.95)×10^12 M_⊙. The modified PME gives a tighter constraint on the virial masses and the inferred dark matter content of galaxy groups and clusters.
ER -