Convolutional Neural Networks for signal detection in real LIGO data
This work addresses the computationally demanding problem of signal detection for gravitational-wave astronomy, though it appears incremental as part of a broader challenge framework.
The paper tackles the challenge of detecting gravitational-wave signals from compact binary mergers in LIGO data by presenting a convolutional neural network (CNN) method, which successfully recovers events from the GWTC-3 catalog in real O3b data.
Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine learning algorithms have been proposed to address current and future challenges. However, the results of these publications often differ greatly due to differing choices in the evaluation procedure. The Machine Learning Gravitational-Wave Search Challenge was organized to resolve these issues and produce a unified framework for machine-learning search evaluation. Six teams submitted contributions, four of which are based on machine learning methods and two are state-of-the-art production analyses. This paper describes the submission from the team TPI FSU Jena and its updated variant. We also apply our algorithm to real O3b data and recover the relevant events of the GWTC-3 catalog.