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Published November 2019 | Submitted + Published
Journal Article Open

A Predicted Correlation Between Age Gradient and Star Formation History in FIRE Dwarf Galaxies

Abstract

We explore the radial variation of star formation histories (SFHs) in dwarf galaxies simulated with Feedback In Realistic Environments (FIRE) physics. The sample contains 26 field dwarf galaxies with M_(star) = 10⁵–10⁹ M⊙. We find age gradients are common in our dwarfs, with older stars dominant at large radii. The strength of the gradient correlates with overall galaxy age such that earlier star formation produces a more pronounced gradient. The relation between formation time and strength of the gradient is driven by both mergers and star formation feedback. Mergers can both steepen and flatten the age gradient depending on the timing of the merger and SFHs of the merging galaxy. In galaxies without significant mergers, feedback pushes stars to the outskirts. The strength of the age gradient is determined by the subsequent evolution of the galaxy. Galaxies with weak age gradients constantly grow to z = 0, meaning that young star formation occurs at a similar radius to which older stars are heated to. In contrast, galaxies with strong age gradients tend to maintain a constant half-mass radius over time. If real galaxies have age gradients as we predict, stellar population studies that rely on sampling a limited fraction of a galaxy can give a biased view of its global SFH. Central fields can be biased young by Gyrs while outer fields are biased old. Fields positioned near the 2D half-light radius will provide the least biased measure of a dwarf galaxy's global SFH.

Additional Information

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2019 September 17. Received 2019 August 18; in original form 2018 December 19. Published: 21 September 2019. We thank the anonymous referee for their insightful comments on the draft. ASG and JSB were supported by NSF AST-1518291, HST-AR-14282, and HST-AR-13888. ASG is further supported by the McDonald Observatory at the University of Texas at Austin, through the Harlan J. Smith fellowship. MBK and AF acknowledge support from NSF grant AST-1517226. MBK also acknowledges support from NSF CAREER grant AST-1752913 and from NASA grants NNX17AG29G and HST-AR-13888, HST-AR-13896, HST-AR-14282, HST-AR-14554, HST-AR-15006, HST-GO-12914, and HST-GO-14191 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. MCC was supported by NSF AST-1815475. DRW is supported by a fellowship from the Alfred P. Sloan Foundation. He also acknowledges support from the Alexander von Humboldt Foundation and from HST-GO-13768, HST-GO-15476, HST-AR-13888, HST-AR-13925, HST-AR-15006, and JWST-ERS-01334. AW was supported by NASA through ATP grant 80NSSC18K1097 and grants HST-GO-14734 and HST-AR-15057 from STScI. RF acknowledges support from the Swiss National Science Foundation (grant no. 157591). CAFG was supported by NSF through grants AST-1517491, AST-1715216, and CAREER award AST-1652522, by NASA through grants NNX15AB22G and 17-ATP17-0067, and by a Cottrell Scholar Award from the Research Corporation for Science Advancement. This work used computational resources of the University of Texas at Austin and the Texas Advanced Computing Center (TACC; http://www.tacc.utexas.edu), the NASA Advanced Supercomputing (NAS) Division and the NASA Center for Climate Simulation (NCCS), and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575.

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Additional details

Created:
August 19, 2023
Modified:
October 20, 2023