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

Understand

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.