Determine the Core Structure and Nuclear Equation of State of Rotating Core-Collapse Supernovae with Gravitational Waves by Convolutional Neural Networks

TL;DR

This study employs convolutional neural networks to classify core rotational rates, rotation length scales, and nuclear equations of state from gravitational wave signals of core-collapse supernovae, achieving high accuracy and demonstrating broad applicability.

Contribution

The paper introduces a CNN-based approach for simultaneous classification of supernova core properties and EoS from GW signals, with transfer learning extending applicability to additional waveform datasets.

Findings

Achieved over 95% accuracy in classifying rotation parameters.

Correctly predicted nuclear EoS groups with 96% accuracy.

Successfully transferred models to new waveform datasets with low error.

Abstract

Detecting gravitational waves from a nearby core-collapse supernova would place meaningful constraints on the supernova engine and nuclear equation of state. Here we use Convolutional Neural Network models to identify the core rotational rates, rotation length scales, and the nuclear equation of state (EoS), using the 1824 waveforms from Richers et al. (2017) for a 12 solar mass progenitor. High prediction accuracy for the classifications of the rotation length scales ($93\%$) and the rotational rates ($95\%$) can be achieved using the gravitational wave signals from -10 ms to 6 ms core bounce. By including additional 48 ms signals during the prompt convection phase, we could achieve $96\%$ accuracy on the classification of four major EoS groups. Combining three models above, we could correctly predict the core rotational rates, rotation length scales, and the EoS at the same time with more than $85\%$ accuracy. Finally, applying a transfer learning method for additional 74 waveforms from FLASH simulations (Pan et al. 2018), we show that our model using Richers' waveforms could successfully predict the rotational rates from Pan's waveforms even for a continuous value with a mean absolute errors of 0.32 rad s$^{-1}$ only. These results demonstrate a much broader parameter regimes our model can be applied for the identification of core-collapse supernova events through GW signals.