Machine Learning the Dark Matter Halo Mass of Milky Way-Like Systems

TL;DR

This paper introduces a neural network-based method to estimate the Milky Way's dark matter halo mass with high precision, overcoming limitations of previous techniques by not relying on equilibrium assumptions or satellite classification.

Contribution

The paper presents a novel neural network approach that accurately estimates halo mass using observable data without assuming dynamical equilibrium or satellite status.

Findings

Achieves less than 0.14 dex uncertainty in mass estimation

Works with simulated halos dissimilar to the Milky Way

Uses observable satellite and environment data

Abstract

Despite the Milky Way's proximity to us, our knowledge of its dark matter halo is fairly limited, and there is still considerable uncertainty in its halo mass. Many past techniques have been limited by assumptions such as the Galaxy being in dynamical equilibrium as well as nearby galaxies being true satellites of the Galaxy, and/or the need to find large samples of Milky Way analogs in simulations.Here, we propose a new technique based on neural networks that obtains high precision ($<0.14$ dex mass uncertainty) without assuming halo dynamical equilibrium or that neighboring galaxies are all satellites, and which can use information from a wide variety of simulated halos (even those dissimilar to the Milky Way) to improve its performance. This method uses only observable information including satellite orbits, distances to nearby larger halos, and the maximum circular velocity of the largest satellite galaxy. In this paper, we demonstrate a proof-of-concept method on simulated dark matter halos; in future papers in this series, we will apply neural networks to estimate the masses of the Milky Way's and M31's dark matter halos, and we will train variations of these networks to estimate other halo properties including concentration, assembly history, and spin axis.