Entropy-based classification of core-collapse supernova gravitational waves for nuclear equation-of-state inference

Gravitational waves from core-collapse supernovae provide a unique probe of the physical processes occurring during stellar collapse and of the properties of dense nuclear matter. In this work we investigate the classification of supernova gravitational-wave signals associated with different nuclear equations of state using an entropy-based machine learning framework. The analysis combines signal decomposition, feature extraction, and feature selection techniques to identify informative characteristics of the waveforms and distinguish between different equation of state models. We evaluate the performance of several machine learning classifiers using a compact set of selected features and assess their robustness in the presence of detector noise. We find that entropy-based representations capture the underlying differences between waveform classes and enable accurate classification while maintaining better performance compared to raw data under noisy conditions. These results demonstrate the potential of complexity-based feature extraction as an efficient and interpretable approach for gravitational wave studies of core-collapse supernovae.