Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

TL;DR

This paper uses machine learning trained on hydrodynamic simulations to predict how baryonic physics affect subhalo populations in dark matter-only simulations, improving the accuracy of subhalo disruption predictions.

Contribution

It introduces a random forest classifier trained on five properties to identify disrupted subhalos, outperforming previous models and capturing baryonic effects in subhalo populations.

Findings

Classifier achieves 85% accuracy in identifying disrupted subhalos.

Predicted subhalo populations match hydrodynamic simulation results.

Baryonic effects cause significant subhalo disruption beyond host-to-host variation.

Abstract

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.