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_____%2F25%3A00645238" target="_blank" >RIV/67985815:_____/25:00645238 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-981-96-1737-1_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-96-1737-1_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-96-1737-1_19" target="_blank" >10.1007/978-981-96-1737-1_19</a>

Result language
angličtina
Original language name
Convolutional Neural Networks for Signal Detection in Real LIGO Data
Original language description
Results of recent publications on machine-learning based gravitational-wave searches vary greatly due to differences in evaluation procedures. The Machine Learning Gravitational-Wave Search Challenge [1] 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 chapter is a modified version of [2], which describes the submission from our team titled TPI FSU Jena and its updated variant. We also apply this 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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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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