On the detection of stellar wakes in the Milky Way: a deep learning approach

TL;DR

This paper explores the use of deep learning to detect dark matter subhalos in the Milky Way by identifying stellar wakes, demonstrating the potential to infer subhalo presence and mass from simulated phase-space data.

Contribution

It introduces a deep learning framework trained on simulations to detect and estimate the mass of dark matter subhalos via stellar wake signatures in the Galactic halo.

Findings

Binary classifier detects subhalos down to 5 x 10^7 solar masses.

Classifier generalizes across different galactocentric distances.

Overdensity and velocity divergence are key features for detection.

Abstract

Due to poor observational constraints on the low-mass end of the subhalo mass function, the detection of dark matter (DM) subhalos on sub-galactic scales would provide valuable information about the nature of DM. Stellar wakes, induced by passing DM subhalos, encode information about the mass of the inducing perturber and thus serve as an indirect probe for the DM substructure within the Milky Way (MW). Our aim is to assess the viability and performance of deep learning searches for stellar wakes in the Galactic stellar halo caused by DM subhalos of varying mass. We simulate massive objects (subhalos) moving through a homogeneous medium of DM and star particles, with phase-space parameters tailored to replicate the conditions of the Galaxy at a specific distance from the Galactic center. The simulation data is used to train deep neural networks with the purpose of inferring both the presence and mass of the moving perturber, and assess subhalo detectability in varying conditions of the Galactic stellar and DM halos. We find that our binary classifier is able to infer the presence of subhalos, showing non-trivial performance down to a subhalo mass of $5 \times 10^7 \rm \, M_\odot$. We also find that our binary classifier is generalisable to datasets describing subhalo orbits at different Galactocentric distances. In a multiple-hypothesis case, we are able to discern between samples containing subhalos of different masses. Out of the phase-space observables available to us, we conclude that overdensity and velocity divergence are the most important features for subhalo detection performance.