Modeling the Impact of Baryons on Subhalo Populations with Machine Learning
[1];
[2];
[3];
[4];
[5]
- Stanford Univ., Stanford, CA (United States)
- Univ. of Pittsburgh, Pittsburgh, PA (United States)
- Stanford Univ., Stanford, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States)
- California Inst. of Technology (CalTech), Pasadena, CA (United States)
- California Inst. of Technology (CalTech), Pasadena, CA (United States); The Observatories of the Carnegie Institution for Science, Pasadena, CA (United States); Univ. of California, Davis, CA (United States)
Here, 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.
- Research Organization:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- AC02-76SF00515
- OSTI ID:
- 1441189
- Report Number(s):
- SLAC-PUB-17274; TRN: US1900872
- Journal Information:
- The Astrophysical Journal (Online), Vol. 859, Issue 2; ISSN 1538-4357
- Publisher:
- Institute of Physics (IOP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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