Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

• Published in: The Astrophysical journal Vol. 859; no. 2; pp. 129 - 145
• Main Authors: Nadler, Ethan O., Mao, Yao-Yuan, Wechsler, Risa H., Garrison-Kimmel, Shea, Wetzel, Andrew
• Format: Journal Article
• Language: English
• Published: Philadelphia The American Astronomical Society 01-06-2018; IOP Publishing; Institute of Physics (IOP)
• Subjects: Algorithms; ASTRONOMY AND ASTROPHYSICS; Astrophysics; Baryons; Classification; Classifiers; Computer simulation; Dark matter; Deposition; Disruption; Galactic disk; Galactic halos; galaxies: abundances; galaxies: halos; Halos; Machine learning; methods: numerical; Milky Way; Physics; Populations; Simulation; Survival
• ISSN: 0004-637X; 1538-4357; 1538-4357
• DOI: 10.3847/1538-4357/aac266

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.