Classifying metal-poor stars with machine learning using nucleosynthesis calculations

TL;DR

This paper demonstrates that machine learning can effectively classify metal-poor stars' nucleosynthesis origins by training on theoretical calculations, achieving 87% accuracy in distinguishing between $s$ and $r$ process signatures and offering insights for observational follow-up.

Contribution

The study introduces a novel application of machine learning trained on nucleosynthesis simulations to classify metal-poor stars' enrichment processes, highlighting its potential and challenges.

Findings

ML classifies $s$ vs. $r$ process stars with 87% accuracy.

ML suggests reclassification of some $i$ process stars.

Method identifies stars needing further observational data.

Abstract

We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculations derived from hydrodynamic simulations for events such as neutron star mergers where the rapid ($r$) neutron capture process can take place. Likewise we consider a bank of calculations from simulations of the slow ($s$) neutron capture process and also consider a few calculations for the intermediate ($i$) neutron capture process. We demonstrate that the ML does well overall in recognizing the $s$ process from the $r$ process, and after training on theoretical calculations ML stellar assignments match conventional labels 87% of the time. We highlight that this method then points to stars that could benefit from additional observational measurements. We also demonstrate that the ML assigns some of the presently considered $i$-process stars to instead be of $r$ or $s$ in origin, but likewise, finds stars currently labeled as $s$ to be potentially more aligned with $i$ enrichment. This first application of ML to classify metal-poor star enrichment using theoretical nucleosynthesis calculations thus reveals the promise, and some challenges, associated with this new data-driven path forward.