Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
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In this paper, we seek to answer the question "given an image of a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?". To answer this question, we employ a deep convolutional neural network to learn visual 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 <cit.>, which has 18 different nuclear EOS, we consider this to be a classic multi-class image classification problem. We attain up to 71% 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.
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