Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning. (arXiv:2203.08161v2 [astro-ph.GA] UPDATED)

The abundance of dark matter (DM) subhalos orbiting a host galaxy is a
generic prediction of the cosmological framework, and is a promising way to
constrain the nature of DM. In this paper, we investigate the use of machine
learning-based tools to quantify the magnitude of phase-space perturbations
caused by the passage of DM subhalos. A simple binary classifier and an anomaly
detection model are proposed to estimate if stars or star particles close to DM
subhalos are statistically detectable …

arxiv astro dark matter deep learning