Gravitational wave (GW) observatories have used template-based search to detect hundreds of compact binary coalescences (CBCs). However, template-based search cannot detect astrophysical sources that lack accurate, computationally tractable waveform models. Here, we present a novel approach for template-free search using coincident anomaly detection (CoAD). CoAD requires neither labeled training examples nor background-only training sets, instead exploiting the coincidence of events across spatially separated detectors as the training loss itself: two neural networks independently analyze data from each detector and are trained to maximize coincident predictions. Additionally, we show that integrated gradient analysis can localize GW signals from the neural network weights, providing a path toward data-driven template construction of unmodeled sources and further improving precision by frequency matching. Using the Codabench dataset of real LIGO backgrounds with injected simulated CBCs and sine-Gaussian low-frequency bursts, CoAD achieves recall up to 0.91 and 0.85, respectively, at a false-alarm rate of one event per year and achieves recall above 0.5 at signal-to-noise ratios below 10. The fully unsupervised nature of CoAD makes it especially well suited for next-generation detectors with greater sensitivity and associated increases in GW event rates.

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• Gravitation
• Gravitational wave detection
• Gravitational wave sources
• Gravitational waves
• Gravitational wave detectors