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. We train our classifier using five properties of each disrupted and
surviving subhalo: pericentric distance and scale factor at first pericentric
passage after accretion, and scale factor, virial mass, and maximum circular
velocity at accretion. Our five-property classifier identifies disrupted
subhalos in the FIRE simulations with an $85\%$ out-of-bag classification
score. We predict surviving subhalo populations in DMO simulations of the FIRE
host halos, finding excellent agreement with the hydrodynamic results; in
particular, our classifier outperforms DMO zoom-in simulations that include the
gravitational potential of the central galactic disk in each hydrodynamic
simulation, indicating that it captures both the dynamical effects of a central
disk and additional baryonic physics. We also predict surviving subhalo
populations for a suite of DMO zoom-in simulations of MW-mass host halos,
finding that baryons impact each system consistently and that the predicted
amount of subhalo disruption is larger than the host-to-host scatter among the
subhalo populations. Although the small size and specific baryonic physics
prescription of our training set limits the generality of our results, our work
suggests that machine-learning classification algorithms trained on
hydrodynamic zoom-in simulations can efficiently predict realistic subhalo
populations.
