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

Source: [Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning](<https://www.sota2.com/research/paper/68fa3de41e209131ce8e3f80>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T01:46:05.638Z

Authors: Matthew C. Edwards

Published: 2020-09-15T22:04:59.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.07367>)

[PDF](<https://arxiv.org/pdf/2009.07367.pdf>)