MADGRAV: a multilevel anomaly-detection pipeline for gravitational-wave searches applied to LIGO data

We present the results of MADGRAV, a deep-learning-based search for high-mass compact binary coalescences, applied to the data collected by the LIGO interferometers during the third observing run and during the first and second part of the fourth observing run. The MADGRAV pipeline consists of a series of sequential convolutional neural networks that perform anomaly detection, glitch classification, coherence testing, and signal ranking. Data from the Hanford and Livingston LIGO detectors are studied (both individually and in coherence) by way of 1 second Q-transform windows. Of the candidates that survive every stage of the pipeline, 48 reach the significance threshold, and we report 47 gravitational wave detections characterised by a false alarm rate below 1,rm yr-1 with a probability of astrophysical origin prm astro>0.9. Of the 47 detections, 44 are shared with the minimally modelled coherent WaveBurst search. The observed total source-frame masses, extracted from official gravitational wave transient catalogues, are in the 14-236 Modot range with a median of 69 Modot, and a median SNR of 16. We note that the recovered fraction of confident detections rises with mass: for LIGO detectors network SNR >10 the pipeline recovers 8.1% of confident catalog events below 30 Modot, 39.8% between 30 and 100 Modot, and 53.3% above 100 Modot, corresponding to 33.3%, 45.5% and 53.3% of the events detected by coherent WaveBurst in the same bins. These results suggest that anomaly detection pipelines can serve as an independent detection channel complementary to matched filtering in the high-mass high-SNR regime.