Convolutional Neural Networks for signal detection in real LIGO data
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917879156310016 |
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| author | Zelenka, Ondřej Brügmann, Bernd Ohme, Frank |
| author_facet | Zelenka, Ondřej Brügmann, Bernd Ohme, Frank |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_07492 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Convolutional Neural Networks for signal detection in real LIGO data Zelenka, Ondřej Brügmann, Bernd Ohme, Frank Instrumentation and Methods for Astrophysics Machine Learning General Relativity and Quantum Cosmology 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. |
| title | Convolutional Neural Networks for signal detection in real LIGO data |
| topic | Instrumentation and Methods for Astrophysics Machine Learning General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2402.07492 |