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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

Matthew C. Edwards This is my paper · ORCID
classification astro-ph.IMgr-qcstat.ML
keywords collapsecorerotatingnuclearequationgravitationalstateanswer
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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. Full citation record

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

    astro-ph.HE 2026-07 conditional novelty 6.5 of 10

    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.

  2. Reconstructing Core-Collapse Supernova Gravitational-Wave Signals with Transdimensional Bayesian Inference

    astro-ph.HE 2026-08 conditional novelty 5.0 of 10

    Transdimensional Bayesian inference with tBilby reconstructs core-collapse supernova gravitational-wave signals in simulated LIGO noise with overlaps up to 85%, and captures the dominant proto-neutron-star mode even a...

  3. 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 of 10

    Using a linear SVM, EOS classification from bounce gravitational waves remains robust to real noise, progenitor diversity, and bounce-time uncertainty in the frequency domain, but collapses in the time domain under ti...

  4. Parameter Estimation Horizon of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors

    astro-ph.HE 2026-08 conditional novelty 4.0 of 10

    A CNN can recover supernova peak frequency out to about 30 kpc and rotation/amplitude out to 200-250 kpc with current networks, extending to roughly 300 kpc and 2-2.5 Mpc with third-generation detectors.

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