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Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

Nadler, Ethan O. and Mao, Yao-Yuan and Wechsler, Risa H. and Garrison-Kimmel, Shea and Wetzel, Andrew (2018) Modeling the Impact of Baryons on Subhalo Populations with Machine Learning. Astrophysical Journal, 859 (2). Art. No. 129. ISSN 1538-4357. https://resolver.caltech.edu/CaltechAUTHORS:20180601-112102390

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/1538-4357/aac266DOIArticle
https://arxiv.org/abs/1712.04467arXivDiscussion Paper
ORCID:
AuthorORCID
Nadler, Ethan O.0000-0002-1182-3825
Mao, Yao-Yuan0000-0002-1200-0820
Wechsler, Risa H.0000-0003-2229-011X
Garrison-Kimmel, Shea0000-0002-4655-8128
Wetzel, Andrew0000-0003-0603-8942
Additional Information:© 2018 The American Astronomical Society. Received 2017 December 12; revised 2018 April 12; accepted 2018 May 1; published 2018 June 1. We have made our code and trained classifier publicly available at github.com/ollienad/subhalo_randomforest; please contact the authors with data requests. We thank Frank van den Bosch and Andrew Hearin for useful discussions. This research was supported in part by NSF grant AST-1517148. Y-YM is supported by the Samuel P. Langley PITT PACC Postdoctoral Fellowship. Support for SG-K was provided by NASA through Einstein Postdoctoral Fellowship grant number PF5-160136 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under contract NAS8-03060. AW was supported by a Caltech-Carnegie Fellowship, in part through the Moore Center for Theoretical Cosmology and Physics at Caltech, and by NASA through grants HST-GO-14734 and HST-AR-15057 from STScI. This research made use of computational resources at SLAC National Accelerator Laboratory, a U.S. Department of Energy Office; the authors are thankful for the support of the SLAC computational team. This research was supported in part by the National Science Foundation under grant No. NSF PHY17-48958 through the Kavli Institute for Theoretical Physics program "The Galaxy-Halo Connection Across Cosmic Time." This research made use of the Python Programming Language, along with many community-developed or maintained software packages, including IPython (Pérez & Granger 2007), Jupyter (jupyter.org), Matplotlib (Hunter 2007), NumPy (van der Walt et al. 2011), Pandas (McKinney 2010), Scikit-Learn (Pedregosa et al. 2011), SciPy (Jones et al. 2001), and Seaborn (seaborn.pydata.org). This research made extensive use of the arXiv and NASA's Astrophysics Data System for bibliographic information.
Group:TAPIR, Moore Center for Theoretical Cosmology and Physics
Funders:
Funding AgencyGrant Number
NSFAST-1517148
Pittsburgh Particle Physics Astrophysics and Cosmology CenterUNSPECIFIED
NASA Einstein FellowshipPF5-160136
NASANAS8-03060
Caltech-Carnegie FellowshipUNSPECIFIED
Caltech Moore Center for Theoretical Cosmology and PhysicsUNSPECIFIED
NASAHST-GO-14734
NASAHST-AR-15057
NSFPHY17-48958
Subject Keywords:dark matter – galaxies: abundances – galaxies: halos – methods: numerical
Issue or Number:2
Record Number:CaltechAUTHORS:20180601-112102390
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180601-112102390
Official Citation:Ethan O. Nadler et al 2018 ApJ 859 129
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:86741
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:01 Jun 2018 18:32
Last Modified:03 Oct 2019 19:47

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