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

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