Convolutional neural networks for signal detection in real LIGO data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F24%3A00587826" target="_blank" >RIV/67985815:_____/24:00587826 - isvavai.cz</a>
Result on the web
<a href="https://hdl.handle.net/11104/0354909" target="_blank" >https://hdl.handle.net/11104/0354909</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevD.110.024024" target="_blank" >10.1103/PhysRevD.110.024024</a>

Result language
angličtina
Original language name
Convolutional neural networks for signal detection in real LIGO data
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10308 - Astronomy (including astrophysics,space science)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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