Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes

TL;DR

This paper demonstrates that machine learning models, especially random forests and auto-encoders, can accurately predict core-collapse supernova explosion outcomes using progenitor data, surpassing traditional criteria.

Contribution

The study introduces novel physics-based and auto-encoder features for predicting supernova explosions, achieving around 90% accuracy, and explores future applications of machine learning in this field.

Findings

Density profiles contain significant explodability information.

Silicon/oxygen and auto-encoder features predict outcomes with ~90% accuracy.

Machine learning can improve supernova explosion predictions.

Abstract

Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with Fornax, we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9$-$27 M$_{\odot}$, we additionally train an auto-encoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and auto-encoder features predict explosion outcome with $\approx$90\% accuracy. In anticipation of much larger multi-dimensional simulation sets, we identify future directions in which machine learning applications will be useful beyond explosion outcome prediction.