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Published August 2, 2021 | Accepted Version
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Empirical Constraints on Core Collapse Supernova Yields using Very Metal Poor Damped Lyman Alpha Absorbers

Abstract

We place empirical constraints on the yields from zero- and low-metallicity core collapse supernovae (CCSNe) using abundances measured in very metal-poor (VMP; [Fe/H] ≤ −2) Damped Lyman Alpha Absorbers (DLAs). For some abundance ratios ([N,Al,S/Fe]), VMP DLAs constrain the metal yields of the first SNe more reliably than VMP stars. We compile a large sample of high-S/N VMP DLAs from over 30 years of literature, most with high resolution spectral measurements. We infer the IMF-averaged CCSNe yield from the median values from the DLA abundance ratios of C, N, O, Al, Si, S, and Fe (over Fe and O). We assume that the DLAs are metal-poor enough that they represent galaxies in their earliest stages of evolution, when CCSNe are the only nucleosynthetic sources of the metals we analyze. We compare five sets of zero- and low-metallicity theoretical yields to the empirical yields derived in this work. We find that the five models agree with the DLA yields for ratios containing Si and S. Only one model, Heger & Woosley (2010, hereafter HW10), reproduced the DLA values for N, and one other model, Limongi & Chieffi (2018, hereafter LC18), reproduced [N/O]. We found little change in the theoretical yields with the adoption of a SN explosion landscape (where certain progenitor masses collapse into black holes, contributing no yields) onto HW10, but fixing explosion energy to progenitor mass results in wide disagreements between the predictions and DLA abundances. We investigate the adoption of a simple, observationally motivated Initial Distribution of Rotational Velocities for LC18 and find a slight improvement.

Additional Information

We thank Tuguldur Sukhbold for their in-depth comments and correspondence that significantly improved this work. We thank Ryan Cooke, Louise Welsh, Donatella Romano, and Lynne Hillenbrand for thoughtful conversations and for sharing information and data that improved the quality of this work. GRANTS and AWARDS and MORE This material is based upon work supported by the National Science Foundation under Grant No. AST-1847909. E.N.K. gratefully acknowledges support from a Cottrell Scholar award administered by the Research Corporation for Science Advancement.

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Created:
August 20, 2023
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October 23, 2023