Published June 1, 2022 | Submitted + Published
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The In Situ Origins of Dwarf Stellar Outskirts in FIRE-2

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Abstract

Extended, old, and round stellar halos appear to be ubiquitous around high-mass dwarf galaxies (10^(8.5) < M_⋆/M_⊙ < 10^(9.6)) in the observed universe. However, it is unlikely that these dwarfs have undergone a sufficient number of minor mergers to form stellar halos that are composed of predominantly accreted stars. Here, we demonstrate that FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulations are capable of producing dwarf galaxies with realistic structures, including both a thick disk and round stellar halo. Crucially, these stellar halos are formed in situ, largely via the outward migration of disk stars. However, there also exists a large population of "nondisky" dwarfs in FIRE-2 that lack a well-defined disk/halo and do not resemble the observed dwarf population. These nondisky dwarfs tend to be either more gas-poor or to have burstier recent star formation histories than the disky dwarfs, suggesting that star formation feedback may be preventing disk formation. Both classes of dwarfs underscore the power of a galaxy's intrinsic shape—which is a direct quantification of the distribution of the galaxy's stellar content—to interrogate the feedback implementation in simulated galaxies.

Additional Information

© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 May 12; revised 2022 April 23; accepted 2022 May 2; published 2022 June 6. E.K.F. thanks Alexander Gurvich for thoughtful comments and discussion that improved the quality of this manuscript. The authors thank the anonymous referee for their helpful comments that have improved the content of this work. J.E.G. gratefully acknowledges support from NSF grant AST-1907723. R.E.S. acknowledges support from NASA grant 19-ATP19-0068 and HST-AR-15809 from the Space Telescope Science Institute (STScI), which is operated by AURA, Inc., under NASA contract NAS5-26555. T.K.C. was supported by Science and Technology Facilities Council (STFC) astronomy consolidated grant ST/P000541/1 and ST/T000244/1. A.W. received support from NASA through ATP grants 80NSSC18K1097 and 80NSSC20K0513; Hubble Space Telescope (HST) grants GO-14734, AR-15057, AR-15809, and GO-15902 from STScI; a Scialog Award from the Heising-Simons Foundation; and a Hellman Fellowship. C.-A.F.-G. was supported by NSF through grants AST-1715216 and CAREER award AST-1652522; by NASA through grant 17-ATP17-0067; by STScI through grant HST-AR-16124.001-A; and by the Research Corporation for Science Advancement through a Cottrell Scholar Award and a Scialog Award. Support for P.F.H. was provided by NSF Research Grants 1911233 & 20009234, NSF CAREER grant 1455342, NASA grants 80NSSC18K0562, HST-AR-15800.001-A. Numerical calculations were run on the Caltech compute cluster "Wheeler," allocations FTA-Hopkins/AST20016 supported by the NSF and TACC, and NASA HEC SMD-16-7592. M.B.K. acknowledges support from NSF CAREER award AST-1752913, NSF grant AST-1910346, NASA grant NNX17AG29G, and HST-AR-15006, HST-AR-15809, HST-GO-15658, HST-GO-15901, HST-GO-15902, HST-AR-16159, and HST-GO-16226 from STScI. Simulations used in this work were run using XSEDE supported by NSF grant ACI-1548562, Blue Waters via allocation PRAC NSF.1713353 supported by the NSF, and NASA High-End Computing Program through the NASA Advanced Supercomputing Division at Ames Research Center. The authors also would like to thank the Flatiron Institute Scientific Computing Core for providing computing resources that made this research possible, and especially for their hard work facilitating remote work during the pandemic. Analysis for this paper was carried out on the Flatiron Institute's computing cluster rusty, which is supported by the Simons Foundation. Softwar e: GizmoAnalysis (Wetzel & Garrison-Kimmel 2020), matplotlib (Hunter 2007), SciPy (Jones et al. 2001), the IPython package (Pérez & Granger 2007), and NumPy (Van Der Walt et al. 2011).

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Created:
August 22, 2023
Modified:
October 23, 2023