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

Excerpt

arXiv:2609.39583v1 Announce Type: cross Abstract: We present the results of \textbf{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 \textbf{MADGRAV} pipeline consists of a series of sequential convolutional neural networks that perform anomaly detection, glitch classification, coherence testing, and signal