Affiliation:
1. Friedrich-Schiller-Universität Jena
2. Michael Stifel Center Jena
3. Astronomical Institute of the Czech Academy of Sciences
4. Max-Planck-Institut für Gravitationsphysik
5. Leibniz Universität Hannover
Abstract
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.
Published by the American Physical Society
2024
Funder
Carl-Zeiss-Stiftung
Akademie Věd České Republiky
European Cooperation in Science and Technology
Gottfried Wilhelm Leibniz Universität Hannover
Max-Planck-Gesellschaft
National Science Foundation
Science and Technology Facilities Council
Australian Research Council
Centre National de la Recherche Scientifique
Instituto Nazionale di Fisica Nucleare
Ministry of Education, Culture, Sports, Science and Technology
Japan Society for the Promotion of Science
National Research Foundation of Korea
Ministry of Science and ICT, South Korea
Academia Sinica
National Science and Technology Council
Max Planck Independent Research Group
State of Niedersachsen
Dutch Nikhef
Publisher
American Physical Society (APS)