Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Sakan, Aknur, Ussipov, Nurzhan, Abdikamalov, Ernazar, Akhmetali, Almat, Zaidyn, Marat, Zhunuskanov, Alisher, Font, José A., Edwards, Matthew C., Abylkairov, Sultan
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Award: G4023665547
OpenAlex ID: W4416190248