Exploring Supernova Gravitational Waves with Machine Learning

TL;DR

This study investigates whether early gravitational wave signals from core-collapse supernovae can reveal the iron core mass, finding limited accuracy and suggesting the need for additional data sources for precise measurements.

Contribution

The paper demonstrates that early GW signals alone are insufficient to accurately determine the iron core mass in supernovae, highlighting the need for incorporating later signals and neutrino data.

Findings

Classification accuracy ~70% for core mass prediction

Early GW signals alone are not enough for precise measurements

Additional information may be necessary for accurate core mass inference

Abstract

Core-collapse supernovae (CCSNe) emit powerful gravitational waves (GWs). Since GWs emitted by a source contain information about the source, observing GWs from CCSNe may allow us to learn more about CCSNs. We study if it is possible to infer the iron core mass from the bounce and early ring-down GW signal. We generate GW signals for a range of stellar models using numerical simulations and apply machine learning to train and classify the signals. We consider an idealized favourable scenario. First, we use rapidly rotating models, which produce stronger GWs than slowly rotating models. Second, we limit ourselves to models with four different masses, which simplifies the selection process. We show that the classification accuracy does not exceed ~70%, signifying that even in this optimistic scenario, the information contained in the bounce and early ring-down GW signal is not sufficient to precisely probe the iron core mass. This suggests that it may be necessary to incorporate additional information such as the GWs from later post-bounce evolution and neutrino observations to accurately measure the iron core mass.