Search Results for author:
Found 3 papers, 1 papers with code
Cataloging Accreted Stars within Gaia DR2 using Deep Learning
The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.
Under the Firelight: Stellar Tracers of the Local Dark Matter Velocity Distribution in the Milky Way
Based on results from Gaia, we estimate that $42 ^{+26}_{-22}\%$ of the local dark matter that is accreted from luminous mergers is in debris flow.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
Modeling the Impact of Baryons on Subhalo Populations with Machine Learning
We identify subhalos in dark matter-only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)-mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
