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

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arxiv 2009.07367 v3 pith:YHNBKOED submitted 2020-09-15 astro-ph.IM gr-qcstat.ML

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

Matthew C. Edwards This is my paper
classification astro-ph.IM gr-qcstat.ML
keywords collapsecorerotatingnuclearequationgravitationalstateanswer
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection

    gr-qc 2026-05 unverdicted novelty 7.0

    A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensi...

  2. The Generalization Gap in Machine Learning EoS Inference from Core-Collapse Supernova Gravitational Waves

    astro-ph.HE 2026-07 conditional novelty 6.5

    LightGBM and other regressors achieve R^{2}≈0.6–0.7 under random CV on CCSN GW catalogues but collapse to worse-than-mean performance under Leave-One-EoS-Out validation, exposing a generalisation gap for unseen EoS families.

  3. Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors

    astro-ph.HE 2026-05 unverdicted novelty 5.0

    Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.

  4. Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves

    astro-ph.HE 2026-03 conditional novelty 4.5

    Real detector noise, multi-progenitor diversity, and 20 ms bounce-time uncertainty leave EOS classification accuracy from CCSN GWs largely intact; larger datasets improve it.