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Probing nuclear physics with supernova gravitational waves and machine learning

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arxiv 2310.15649 v2 pith:AMGZK5D4 submitted 2023-10-24 astro-ph.HE

Probing nuclear physics with supernova gravitational waves and machine learning

Ayan Mitra , Daniil Orel , Y. Sultan Abylkairov , Bekdaulet Shukirgaliyev , Ernazar Abdikamalov This is my paper
classification astro-ph.HE
keywords modelssignalscaptureclassifyelectroneossgravitationalsupernovae
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Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models.

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Cited by 3 Pith papers

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  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. 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.

  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

    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.