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Preprint · arXiv · November 11, 2025

Probing Supernovae through gravitational wave entropy

Aknur Sakan, Nurzhan Ussipov, Ernazar B. Abdikamalov, Almat Akhmetali, Marat Zaidyn, Alisher Zhunuskanov

Nurzhan Ussipov
Read on arXivPDFDOI 10.48550/arxiv.2511.08010

Abstract

We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we compute four entropy measures -- Shannon, exponential, Rényi, and Tsallis -- and apply three feature selection methods to identify the most informative features. We then train machine-learning classifiers on these features to compare the performance of different methodological combinations. We find that the combination of Rényi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals.

Pulsars and Gravitational Waves ResearchGamma-ray bursts and supernovaeSeismology and Earthquake Studiescore-collapse supernovaegravitational wave signalsentropy measures

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