CASPER: Interpretable ResNet based Classifier with FastShap Explainer for Gravitational Wave Detection

Abstract

Traditional matched filtering has been the standard for Gravitational waves (GW) detection ever since LIGO was established, even though it requires pre-computed waveform templates and provides no accounts of information about which signal drove the decision of classification. Deep-learning alternatives showed competitive sensitivity, but system biasesincluding class overlap, imbalanced class weighting, limited sample variation, and traintest mismatchcontinue to cause problems with generalisation in real detector noise. We introduce CASPER-Classification with Attribution via ShaPlEy in Residual neural networks, an end-to-end pipeline combining residual convolutional neural network (CNN) classifier with a FastSHAP explainer. 260 distinct events from the Gravitational Wave open Science Centre were fetched across SNR range of 7-42 from both H1 and L1 detectors with no synthetic augmentation. The classifier achieves AUC (Area Under Curve) of 91% across the model with a low false alarm rate. Focal Loss and Platt Calibration were used to improve decision boundary and generalisation. FastSHAP attribution maps recover the complete chirp morphology and provides detailed maps for a visual interpretation of the decision. The complete pipeline contains fewer parameters than standard deep learning models and requires no hardware except a standard CPU making our model an effective lightweight pipeline for Gravitational Wave Detection under real life conditions.