Predictions for complex distributions of stellar elemental abundances in low-mass galaxies
Abstract
We investigate stellar elemental abundance patterns at z = 0 in eight low-mass (M_∗ = 10⁶−10⁹ M_⊙) galaxies in the Feedback in Realistic Environments cosmological simulations. Using magnesium (Mg) as a representative α-element, we explore stellar abundance patterns in magnesium-to-iron ([Mg/Fe]) versus iron-to-hydrogen ([Fe/H]), which follow an overall monotonic trend that evolved slowly over time. Additionally, we explore three notable secondary features in enrichment (in three different case-study galaxies) that arise from a galaxy merger or bursty star formation. First, we observe a secondary track with a lower [Mg/Fe] than the main trend. At z = 0, stars from this track are predominantly found within 2–6 kpc of the centre; they were accreted in a 1:3 total-mass-ratio merger ∼0.4 Gyr ago. Second, we find a distinct elemental bimodality that forms following a strong burst in star formation in a galaxy at t_(lookback) ∼ 10 Gyr. This burst quenched star formation for ∼0.66 Gyr, allowing Type Ia supernovae to enrich the system with iron (Fe) before star formation resumed. Third, we examine stripes in enrichment that run roughly orthogonal to the dominant [Mg/Fe] versus [Fe/H] trend; these stripes correspond to short bursts of star formation during which core-collapse supernovae enrich the surrounding medium with Mg (and Fe) on short time-scales. If observed, these features would substantiate the utility of elemental abundances in revealing the assembly and star-formation histories of dwarf galaxies. We explore the observability of these features for upcoming spectroscopic studies. Our results show that precise measurements of elemental abundance patterns can reveal critical events in the formation histories of low-mass galaxies.
Additional Information
© 2022 The Author(s) Published by Oxford University Press on behalf of 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). We thank the anonymous referee, Jenna Samuel, and Anna Parul for useful comments. Support for PBP was provided by the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the State of Illinois Department of Human Services. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. AW received support from: NSF via CAREER award AST-2045928 and grant AST-2107772; NASA ATP grants 80NSSC18K1097 and 80NSSC20K0513; HST grants GO-14734, AR-15057, AR-15809,GO-15902, and GO-16273 from STScI; a Scialog Award from the Heising-Simons Foundation; and a Hellman Fellowship from Hellman Foundation. CAFG was supported by NSF through grants AST-1715216 and AST-2108230, 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. We performed this work in part at the Aspen Center for Physics, supported by NSF grant PHY-1607611. We ran simulations using: XSEDE, supported by NSF grant ACI-1548562; Blue Waters, supported by the NSF; Pleiades, via the NASA HEC program through the NAS Division at Ames Research Center. We used the publicly available Python packages, gizmoanalysis (Wetzel & Garrison-Kimmel 2020a) and haloanalysis (Wetzel & Garrison-Kimmel 2020b) to analyse data. DATA AVAILABILITY. All Python codes that we used to generate these figures, including the data content of the figures, are available at https://github.com/patelpb96. FIRE-2 simulations are publicly available (Wetzel et al. 2022) at http://flathub.flatironinstitute.org/fire. Additional FIRE simulation data are available at https://fire.northwestern.edu/data. A public version of the gizmo code is available at http://www.tapir.caltech.edu/~phopkins/Site/GIZMO.html.Additional details
- Eprint ID
- 118723
- Resolver ID
- CaltechAUTHORS:20230105-895833000.47
- NSF
- OCI-0725070
- NSF
- ACI-1238993
- Illinois Department of Human Services
- NSF
- AST-2045928
- NSF
- AST-2107772
- NASA
- 80NSSC18K1097
- NASA
- 80NSSC20K0513
- NASA
- HST-GO-14734
- NASA
- HST-AR-15057
- NASA
- HST-AR-15809
- NASA
- HST-GO-15902
- NASA
- HST-GO-16273
- Heising-Simons Foundation
- Scialog Award
- Hellman Fellowship
- NSF
- AST-1715216
- NSF
- AST-2108230
- NSF
- AST-1652522
- NASA
- 17-ATP17-0067
- NASA
- HST-AR-16124.001-A
- Cottrell Scholar of Research Corporation
- NSF
- PHY-1607611
- NSF
- ACI-1548562
- Created
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2023-01-08Created from EPrint's datestamp field
- Updated
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2023-01-08Created from EPrint's last_modified field