Determining Satellite Infall Times Using Machine Learning
Stan Barmentloo, Marius Cautun (Leiden Observatory)

TL;DR
This paper introduces a neural network method trained on simulations to accurately determine the infall times of Milky Way satellite galaxies, revealing insights into their accretion history and quenching processes.
Contribution
A novel neural network approach trained on EAGLE simulations to predict satellite infall times and classify backsplash galaxies with high accuracy.
Findings
All satellites within 300 kpc have been inside the Galactic halo.
The MW satellite accretion rate matches theoretical predictions except for a recent peak.
Quenching times for ultrafaint dwarfs are independent of infall time.
Abstract
A key unknown of the Milky Way (MW) satellites is their orbital history, and, in particular, the time they were accreted onto the MW system since it marks the point where they experience a multitude of environmental processes. We present a new methodology for determining infall times, namely using a neural network (NN) algorithm. The NN is trained on MW-analogues in the EAGLE hydrodynamical simulation to predict if a dwarf galaxy is at first infall or a backsplash galaxy and to infer its infall time. The resulting NN predicts with 85\% accuracy if a galaxy currently outside the virial radius is a backsplash satellite and determines the infall times with a typical 68\% confidence interval of 4.4 Gyrs. Applying the NN to MW dwarfs with Gaia EDR3 proper motions, we find that all of the dwarfs within 300 kpc had been inside the Galactic halo. The overall MW satellite accretion rate agrees…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Stellar, planetary, and galactic studies · Astronomical Observations and Instrumentation
