UniverseMachine: Predicting Galaxy Star Formation over Seven Decades of Halo Mass with Zoom-in Simulations

Yunchong Wang, Ethan O. Nadler, Yao Yuan Mao, Susmita Adhikari, Risa H. Wechsler, Peter Behroozi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We apply the empirical galaxy-halo connection model UniverseMachine to dark-matter-only zoom-in simulations of isolated Milky Way (MW)-mass halos, along with their parent cosmological simulations. This application extends UniverseMachine predictions into the ultrafaint dwarf galaxy regime (102 M o˙ ≤ M ∗ ≤ 105 M o˙) and yields a well-resolved stellar mass-halo mass (SMHM) relation over the peak halo mass range of 108-1015 M o˙. The extensive dynamic range provided by the zoom-in simulations allows us to assess specific aspects of dwarf galaxy evolution predicted by UniverseMachine. In particular, although UniverseMachine is not constrained for dwarf galaxies with M ∗ ≲ 108 M o˙, our predicted SMHM relation is consistent with that inferred for MW satellite galaxies at z = 0 using abundance matching. However, UniverseMachine predicts that nearly all galaxies are actively star-forming below M ∗ ∼ 107 M o˙ and that these systems typically form more than half of their stars at z ≲ 4, which is discrepant with the star formation histories of Local Group dwarf galaxies that favor early quenching. This indicates that the current UniverseMachine model does not fully capture galaxy quenching physics at the low-mass end. We highlight specific improvements necessary to incorporate environmental and reionization-driven quenching for dwarf galaxies, and we provide a new tool to connect dark matter accretion to star formation over the full dynamic range that hosts galaxies.

Original languageEnglish (US)
Article number116
JournalAstrophysical Journal
Volume915
Issue number2
DOIs
StatePublished - Jul 10 2021
Externally publishedYes

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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