Probing Supernovae through gravitational wave entropy

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.