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Published February 20, 2020 | Submitted + Published
Journal Article Open

Simulating Metal Mixing of Both Common and Rare Enrichment Sources in a Low-mass Dwarf Galaxy

Abstract

One-zone models constructed to match observed stellar abundance patterns have been used extensively to constrain the sites of nucleosynthesis with sophisticated libraries of stellar evolution and stellar yields. The metal mixing included in these models is usually highly simplified, although it is likely to be a significant driver of abundance evolution. In this work we use high-resolution hydrodynamics simulations to investigate how metals from individual enrichment events with varying source energies E_(ej) mix throughout the multiphase interstellar medium (ISM) of a low-mass (M_(gas) = 2 × 10⁶ M_⊙), low-metallicity, isolated dwarf galaxy. These events correspond to the characteristic energies of both common and exotic astrophysical sites of nucleosynthesis, including asymptotic giant branch winds (E_(ej) ~ 10⁴⁶ erg), neutron star–neutron star mergers (E_(ej) ~ 10⁴⁹ erg), supernovae (E_(ej) ~ 10⁵¹ erg), and hypernovae (E_(ej) ~ 10⁵² erg). We find the mixing timescales for individual enrichment sources in our dwarf galaxy to be long (100 Myr–1 Gyr), with a clear trend of increasing homogeneity for the more energetic events. Given these timescales, we conclude that the spatial distribution and frequency of events are important drivers of abundance homogeneity on large scales; rare, low-E_(ej) events should be characterized by particularly broad abundance distributions. The source energy E_(ej) also correlates with the fraction of metals ejected in galactic winds, ranging anywhere from 60% at the lowest energy to 95% for hypernovae. We conclude by examining how the radial position, local ISM density, and global star formation rate influence these results.

Additional Information

© 2020. The American Astronomical Society. Received 2019 September 9; revised 2020 January 3; accepted 2020 January 18; published 2020 February 25. We would like to thank Brian O'Shea, Benoit Côté, Kathryn V. Johnston, and Jason Tumlinson for valuable discussions and comments on a previous version of this work that appeared as a chapter in the first author's dissertation. In addition, we thank the anonymous referee, whose comments have significantly improved this work. A.E. was supported by a Blue Waters Graduate Fellowship. G.L.B. acknowledges support from NSF grants AST-1615955 and OAC-1835509 and NASA grant NNX15AB20G. M.-M.M.L. was partly supported by NSF grant AST18-15461. We gratefully recognize computational resources provided by NSF XSEDE through grant No. TGMCA99S024, the NASA High-End Computing Program through the NASA Advanced Supercomputing Division at Ames Research Center, Columbia University, and the Flatiron Institute. This work made significant use of many open-source software packages. These are products of collaborative effort by many independent developers from numerous institutions around the world. Their commitment to open science has helped make this work possible. Software: yt (Turk et al. 2011), Enzo (Bryan et al. 2014), Grackle (Smith et al. 2017), Python (Van Rossum & Drake 1995), IPython (Pérez & Granger 2007), NumPy (Oliphant 2006), SciPy (Virtanen et al. 2020), Matplotlib (Hunter 2007), HDF5 (The HDF Group 1997), h5py (Collette et al. 2017), Astropy (Astropy Collaboration et al. 2013; Price-Whelan et al. 2018), Cloudy (Ferland et al. 2013), and deepdish.

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Published - Emerick_2020_ApJ_890_155.pdf

Submitted - 1909.04695.pdf

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Additional details

Created:
August 22, 2023
Modified:
October 19, 2023