Learning and interpreting gravitational-wave features from CNNs with a random forest approach

Abstract Convolutional neural networks (CNNs) have attracted increasing attention in gravitational wave (GW) data analysis due to their ability to automatically learn hierarchical features from raw strain data. However, the physical meaning of these learned features remains underexplored, limiting the interpretability of such models. In this work, we propose a hybrid architecture that combines a CNN-based feature extractor with a random forest (RF) classifier to improve both detection performance and interpretability. Unlike prior approaches that directly connect classifiers to CNN outputs, our method introduces four physically interpretable metrics-variance, signal-to-noise ratio (SNR), waveform overlap, and peak amplitude-computed from the final convolutional layer. These are jointly used with the CNN output probability in the RF classifier to enable more informed decision boundaries. Tested on long-duration strain datasets, our hybrid model outperforms a baseline-CNN model, achieving a relative improvement of 21% in sensitivity at a fixed false alarm rate of 10 events per month. Notably, it also shows improved detection of low-SNR signals (SNR <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mtext>⩽</mml:mtext> </mml:mrow> </mml:math> 10), which are especially vulnerable to misclassification in noisy environments. Feature importance analysis and ablation studies reveal that handcrafted features play a significant role in classification decisions and contribute to improved performance. These findings suggest that physically motivated post-processing of CNN feature maps can serve as a valuable tool for interpretable and efficient GW detection, bridging the gap between deep learning and domain knowledge.