AI-Driven Gravitational Wave Detection Achieves High Recall Rates

This paper introduces a novel approach to gravitational wave detection using coincident anomaly detection (CoAD). Unlike traditional template-based searches, CoAD does not require pre-existing waveform models, making it suitable for detecting astrophysical sources with poorly understood waveforms, such as core-collapse supernovae or cosmic strings. The method employs neural networks to independently analyze data from multiple detectors, training them to maximize coincident predictions. This unsupervised learning approach eliminates the need for labeled training data or background-only training sets. The integrated gradient analysis further allows for the localization of gravitational wave signals and data-driven template construction. The results demonstrate high recall rates for both compact binary coalescences (CBCs) and sine-Gaussian bursts, even at low signal-to-noise ratios. The fully unsupervised nature of CoAD makes it particularly promising for future gravitational wave observatories with increased sensitivity and event rates. However, the reliance on neural networks introduces potential biases and requires careful validation.

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