Exploring metallicity-dependent rates of Type Ia supernovae and their impact on galaxy formation
Abstract
Type Ia supernovae play a critical role in stellar feedback and elemental enrichment in galaxies. Recent transient surveys like the All-Sky Automated Survey for Supernova (ASAS-SN) and the Dark Energy Survey (DES) find that the specific Ia rate at z ~ 0 may be ~ 15-50 times higher in lower-mass galaxies than at Milky Way-mass. Independently, Milky Way observations show that the close-binary fraction of solar-type stars is higher at lower metallicity. Motivated by these observations, we use the FIRE-2 cosmological zoom-in simulations to explore the impact of varying Ia rate models, including metallicity dependence, on galaxies across a range of stellar masses: 10⁷ M_⊙ - 10¹¹ M_⊙. First, we benchmark our simulated star-formation histories (SFHs) against observations. We show that assumed SFHs and stellar mass functions play a major role in determining the degree of tension between observations and metallicity-independent Ia rate models, and potentially cause ASAS-SN and DES observations to be much more consistent with each other than might naively appear. Models in which the Ia rate increases with decreasing metallicity (as ~ Z^(-0.5) to Z⁻¹) provide significantly better agreement with observations. Encouragingly, these increases in Ia rate (> 10x in low-mass galaxies) do not significantly impact galaxy stellar masses and morphologies: effective radii, axis ratios, and ν/σ remain largely unaffected except for our most extreme rate models. We explore implications for both [Fe/H] and [α/Fe] enrichment: metallicity-dependent Ia rate models can improve agreement with observed stellar mass-metallicity relations in low-mass galaxies. Our results demonstrate that a wide range of metallicity-dependent Ia models are viable for galaxy formation and motivate future work in this area.
Additional Information
Attribution 4.0 International (CC BY 4.0). We thank Peter Behroozi, Ivanna Escala, Shea Garrison-Kimmel, Robyn Sanderson, Dan Weisz, and Philip Wiseman for valuable discussions that improved this paper overall, as well as sharing data in some cases. This analysis relied on NumPy (Harris et al. 2020), SciPy (Jones et al. 2001; Virtanen et al. 2020), AstroPy, a community-developedcore Python package for Astronomy (Astropy Collaboration et al. 2013, 2018), Matplotlib, a Python library for publication-quality graphics (Hunter 2007), the IPython package (Pérez & Granger 2007), and the publicly available package GizmoAnalysis (Wetzel & Garrison-Kimmel 2020, available at https://bitbucket.org/awetzel/gizmo_analysis); as well as NASA's Astrophysics Data System (ADS) and the arXiv preprint service. PJG and AW received support from: the NSF via CAREER award AST-2045928 and grant AST-2107772; NASA ATP grants 80NSSC18K1097 and 80NSSC20K0513; 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. AW and BJS acknowledge the Scialog Fellows program, sponsored by the Research Corporation for Science Advancement, which motivated some of this work. Support for PFH was provided by NSF Research Grants 1911233 & 20009234, NSF CAREER grant 1455342, NASA grants 80NSSC18K0562, HST-AR-15800.001-A. BJS received support from NSF grants AST-1920392, AST-1911074, AST-1908952, and AST-2050710 and NASA grants HST-GO-16451, HST-GO-16498, and 80NSSC21K1788. CW acknowledges support from NSF LEAPS-MPS grant AST-2137988. CAFG received support from NSF grants AST-1715216, AST-2108230, and CAREER award AST-1652522; from NASA grant 17-ATP17-0067; from STScI through grant HST-AR-16124.001-A; and from the Research Corporation for Science Advancement through a Cottrell Scholar Award. We ran simulations and performed numerical calculations using: the UC Davis computer cluster Peloton, the Caltech computer cluster Wheeler, the Northwestern computer cluster Quest; XSEDE, supported by NSF grant ACI-1548562; Blue Waters, supported by the NSF; Frontera allocations FTA/Hopkins-AST21010 and AST20016, supported by the NSF and TACC; XSEDE allocations TG-AST140023 and TG-AST140064, and NASA HEC allocations SMD-16-7561, SMD-17-1204, and SMD-16-7592; Pleiades, via the NASA HEC program through the NAS Division at Ames Research Center. DATA AVAILABILITY.The python code and data tables used to create each figure are available at https://github.com/pratikgandhi95/Ia-rates-metallicity-dependence. The FIRE-2 simulations are publicly available (Wetzel et al. 2022) at http://flathub.flatironinstitute.org/fire. Additional FIRE simulation data is 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.Attached Files
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Additional details
- Eprint ID
- 116342
- Resolver ID
- CaltechAUTHORS:20220816-222045764
- AST-2045928
- NSF
- AST-2107772
- NSF
- 80NSSC18K1097
- NASA
- 80NSSC20K0513
- NASA
- HST-GO-14734
- NASA
- HST-AR-15057
- NASA
- HST-AR-15809
- NASA
- HST-GO-15902
- NASA
- Scialog Award
- Heising-Simons Foundation
- Hellman Fellowship
- Scialog Fellow of Research Corporation
- AST-1911233
- NSF
- AST-20009234
- NSF
- AST-1455342
- NSF
- 80NSSC18K0562
- NASA
- HST-AR-15800.001-A
- NASA
- AST-1920392
- NSF
- AST-1911074
- NSF
- AST-1908952
- NSF
- AST-2050710
- NSF
- HST-GO-16451
- NASA
- HST-GO-16498
- NASA
- 80NSSC21K1788
- NASA
- AST-2137988
- NSF
- AST-1715216
- NSF
- AST-2108230
- NSF
- AST-1652522
- NSF
- 17-ATP17-0067
- NASA
- HST-AR-16124.001-A
- NASA
- Cottrell Scholar of Research Corporation
- Created
-
2022-08-17Created from EPrint's datestamp field
- Updated
-
2023-06-02Created from EPrint's last_modified field
- Caltech groups
- Astronomy Department, TAPIR