Towards an anomaly detection pipeline for gravitational waves at the Einstein telescope

We present the implementation of an anomaly–detection algorithm based on a deep convolutional autoencoder for the search for gravitational waves (GWs) in time-frequency spectrograms. Our method targets short-duration ( ≲ 2 s) GW signals, exemplified by mergers of compact objects forming or involving an intermediate-mass black hole (IMBH). Such short signals are difficult to distinguish from background noise; yet their brevity makes them well-suited to machine-learning analyses with modest computational requirements. Using the data from the Einstein Telescope Mock Data Challenge as a benchmark, we demonstrate that the approach can successfully flag GW-like transients as anomalies in interferometer data of a single detector, achieving an initial detection efficiency of 23% for injected signals corresponding to IMBH–forming mergers. After introducing weak supervision, the model exhibits excellent generalisation and recovers all injected IMBH–forming mergers, independent of their total mass or signal–to–noise ratio, with a false–alarm rate due to statistical noise fluctuations of approximately 4.5 events per year for a single interferometer operating with a 100% duty cycle. The method also successfully identifies lower–mass mergers leading to the formation of black holes with mass larger than ≃ 20 M ⊙ . Our pipeline does not yet classify anomalies, distinguishing between actual GW signals and noise artefacts; however, it highlights any deviation from the learned background noise distribution for further scrutiny. These results demonstrate that anomaly detection offers a powerful, model–independent framework for future GW searches, paving the way toward fully automated and adaptive analysis pipelines.