Possibilities and Limitations of Kinematically Identifying Stars from Accreted Ultra-Faint Dwarf Galaxies

Kavli Affiliate: Anna Frebel

| First 5 Authors: Kaley Brauer, Hillary D. Andales, Alexander P. Ji, Anna Frebel, Mohammad K. Mardini

| Summary:

The Milky Way has accreted many ultra-faint dwarf galaxies (UFDs), and stars
from these galaxies can be found throughout our Galaxy today. Studying these
stars provides insight into galaxy formation and early chemical enrichment, but
identifying them is difficult. Clustering stellar dynamics in 4D phase space
($E$, $L_z$, $J_r$, $J_z$) is one method of identifying accreted structure. We
produce 32 simulated stellar halos using particle tagging with the
textit{Caterpillar} simulation suite and thoroughly test the abilities of
different clustering algorithms to recover tidally disrupted UFD remnants. We
perform over 10,000 clustering runs, testing seven clustering algorithms,
roughly twenty hyperparameter choices per algorithm, and six different types of
data sets each with up to 32 simulated samples. Of the seven algorithms,
HDBSCAN most consistently balances UFD recovery rates and cluster realness
rates. We find that even in highly idealized cases, the vast majority of
clusters found by clustering algorithms do not correspond to real accreted UFD
remnants and we can generally only recover $6%$ of UFDs remnants at best.
These are remnants that accreted recently, $z_{text{accretion}}lesssim 0.5$.
Based on these results, we make recommendations to help guide the search for
dynamically-linked clusters of UFD stars in observational data. We find that
real clusters generally have higher median energy and $J_r$, providing a way to
help identify real vs. fake clusters. We also recommend incorporating chemical
tagging as a way to improve clustering results.

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