Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

12 Dec 2017Ethan O. NadlerYao-Yuan MaoRisa H. WechslerShea Garrison-KimmelAndrew Wetzel

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... (read more)

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