Probing Supernovae through gravitational wave entropy

TL;DR

This paper introduces an entropy-based analysis framework for gravitational-wave signals from supernovae, utilizing multiple entropy measures and machine learning to improve signal classification accuracy.

Contribution

It presents a novel approach combining entropy measures, feature selection, and machine learning for analyzing supernova gravitational-wave signals.

Findings

R'enyi entropy with wavelet domain is most effective for signal discrimination.

Feature selection improves classification performance.

Machine learning classifiers can distinguish different supernova signals effectively.

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.