Details
| Original language | English |
|---|---|
| Article number | 024024 |
| Number of pages | 11 |
| Journal | Physical Review D |
| Volume | 110 |
| Issue number | 2 |
| Publication status | Published - 10 Jul 2024 |
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.
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Nuclear and High Energy Physics
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In: Physical Review D, Vol. 110, No. 2, 024024, 10.07.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Convolutional neural networks for signal detection in real LIGO data
AU - Zelenka, Ondřej
AU - Brügmann, Bernd
AU - Ohme, Frank
N1 - Publisher Copyright: © 2024 authors.
PY - 2024/7/10
Y1 - 2024/7/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85198606725&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2402.07492
DO - 10.48550/arXiv.2402.07492
M3 - Article
AN - SCOPUS:85198606725
VL - 110
JO - Physical Review D
JF - Physical Review D
SN - 2470-0010
IS - 2
M1 - 024024
ER -