On the Optimal Choice of Nucleosynthetic Yields, IMF and Number of SNe Ia for Chemical Evolution Modelling

TL;DR

This paper develops a scoring system to evaluate nucleosynthetic yield tables in galactic chemical evolution models, identifying the best yields for reproducing proto-solar abundances and improving simulation predictions.

Contribution

It introduces a Bayesian and cross-validation based scoring method to select optimal nucleosynthetic yields and demonstrates how to enhance chemical evolution modeling accuracy.

Findings

Prantzos et al. (P18) yields best for all elements.

Chieffi & Limongi (C04) yields best for key elements.

Inferred SSP parameters are consistent across yield tables.

Abstract

To fully harvest the rich library of stellar elemental abundance data available, we require reliable models that facilitate our interpretation of them. Galactic chemical evolution (GCE) models are one such set, and a key part of which are the selection of chemical yields from different nucleosynthetic enrichment channels, predominantly asymptotic giant branch (AGB) stars, Type Ia supernovae (SNe Ia), and core-collapse supernovae (CC-SNe). Here, we present a scoring system for yield tables based on their ability to reproduce proto-solar abundances within a simple parametrisation of the GCE modelling software Chempy, which marginalises over galactic parameters describing simple stellar populations (SSPs) and interstellar medium physics. Two statistical scoring methods are presented, based on Bayesian evidence and leave-one-out cross-validation and are applied to five CC-SN tables, (a) for all mutually available elements and (b) for a subset of the 9 most abundant elements. We find that the yields of Prantzos et al. (P18, including stellar rotation) and Chieffi & Limongi (C04) best reproduce proto-solar abundances for the two cases, respectively. The inferred best-fit SSP parameters for (b) are $\alpha_\mathrm{IMF}=-2.45^{+0.15}_{-0.11}$ for the initial mass function high-mass slope and $\mathrm{N}_\mathrm{Ia}=1.29^{+0.45}_{-0.31}\times10^{-3}$ M$_\odot^{-1}$ for the SN Ia normalisation, which are broadly consistent across tested yield tables. Additionally, we demonstrate how Chempy can be used to dramatically improve elemental abundance predictions of hydrodynamical simulations by plugging tailored best-fit SSP parameters into a Milky Way analogue from Gutcke & Springel. Our code, including a comprehensive tutorial, is freely available and can additionally provide SSP enrichment tables for any combination of parameters and yield tables.