Probing Supernovae through gravitational wave entropy

Aknur Sakan; Nurzhan Ussipov; Ernazar Abdikamalov; Almat Akhmetali; Marat Zaidyn; Alisher Zhunuskanov; José A. Font; Matthew C. Edwards; Sultan Abylkairov

arXiv · 2511.08010

Probing Supernovae through gravitational wave entropy

Aknur Sakan·Nurzhan Ussipov·Ernazar Abdikamalov·Almat Akhmetali·Marat Zaidyn·Alisher Zhunuskanov·José A. Font·Matthew C. Edwards·Sultan Abylkairov

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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, R\'enyi, 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 R\'enyi entropy from the wavelet domain and the Relief-F selection method yields the most effective discrimination among different gravitational-wave signals.

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Aknur Sakan, Nurzhan Ussipov, Ernazar Abdikamalov, Almat Akhmetali, Marat Zaidyn, Alisher Zhunuskanov, José A. Font, Matthew C. Edwards, Sultan Abylkairov. 2025-11-11. Probing Supernovae through gravitational wave entropy. https://arxiv.org/abs/2511.08010

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