★ BOAW: a neural network approach to combining multiple pipelines in gravitational-wave searches ★

About

Gravitational-wave transient searches in LIGO-Virgo-KAGRA (LVK) routinely run multiple low-latency (and offline) pipelines in parallel. Their redundancy and complementarity improves robustness, but it also creates a practical challenge: how to combine pipeline outputs into a single, reliable detection statistic without resorting to mere union of their findings. We present BOAW (Best-Of-All-Worlds), a machine-learning meta-pipeline that fuses evidence from four Compact Binary Coalescence search pipelines (GstLAL, MBTA, PyCBC, and cWB-BBH) used in the LVK for real-time astronomical alert generation, but also in offline catalogs of gravitational-wave detections. BOAW ingests a compact, interpretable feature set from each pipeline and treats non-triggers (from any of the aforementioned pipelines) as informative rather than missing data. We use an ensemble of five diverse neural-network architectures, selected through neural architecture search and trained on LVK mock-data-challenge injections and real instrument noise in order to produce a unified detection statistic. On held-out data, BOAW achieves an AUC of 0.99, delivering up to an order-of-magnitude reduction in false-positive rate at 90% detection efficiency compared to individual pipelines and their logical combinations. The overall algorithmic architecture is flexible, computationally light-weight for training and can be deployed for real-time public alert generation as well as for offline analysis (event catalog generation) without adding any measurable latency. We propose its adoption by the LVK for its ongoing observations.

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