Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
This work addresses the challenge of classifying nuclear equations of state from gravitational wave signals, which is incremental as it applies existing deep learning methods to a specific astrophysical dataset.
The paper tackled the problem of determining the nuclear equation of state from rotating core collapse gravitational wave signals using deep convolutional neural networks, achieving up to 72% correct classifications and 97% in top-5 accuracy on a test set.
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.