Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning

TL;DR

This study uses deep learning to classify the nuclear equation of state from rotating core collapse gravitational wave signals, achieving up to 72% accuracy and demonstrating measurable dependence of signals on the EOS.

Contribution

It introduces a deep learning approach to classify the nuclear EOS from gravitational wave signals, a novel application in this context.

Findings

Achieved up to 72% correct classification accuracy.

Top 5 accuracy reaches 97%, indicating strong signal dependence.

Demonstrated measurable correlation between GW signals and nuclear EOS.

Abstract

In this paper, we seek to answer the question "given a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?". To answer this question, we employ deep convolutional neural networks to learn visual and temporal patterns embedded within rotating core collapse gravitational wave (GW) signals in order to predict the nuclear equation of state (EOS). Using the 1824 rotating core collapse GW simulations by Richers et al. (2017), which has 18 different nuclear EOS, we consider this to be a classic multi-class image classification and sequence classification problem. We attain up to 72\% correct classifications in the test set, and if we consider the "top 5" most probable labels, this increases to up to 97\%, demonstrating that there is a moderate and measurable dependence of the rotating core collapse GW signal on the nuclear EOS.