[en] We present a machine learning based pipeline to analyze unmodeled gravitational wave (GW) transients of less than 10 s. The convolutional neural network (CNN) is based on a U-NET architecture and takes as input data from GW interferometers represented as time-frequency maps, returning a spectrogram without the background noise. The CNN has been trained on simulated data, using a generated Gaussian background noise and injecting GW signals from core-collapse supernovae (CCSNe) simulations. The pipeline is able to successfully denoise spectrograms and recognize as signals also CCSNe waveforms for which it has not been trained on.
Disciplines :
Aérospatiale, astronomie & astrophysique
Auteur, co-auteur :
Pracchia, Matteo ; Université de Liège - ULiège > Unités de recherche interfacultaires > Space sciences, Technologies and Astrophysics Research (STAR)
Langue du document :
Anglais
Titre :
Adaptation of a Convolutional Neural Network-based Pipeline to Detect Short GravitationalWave Bursts
Date de publication/diffusion :
décembre 2024
Nom de la manifestation :
41st Liège International Astrophysical Colloquium
Lieu de la manifestation :
Liège, Belgique
Date de la manifestation :
15-19 July 2024
Sur invitation :
Oui
Manifestation à portée :
International
Titre du périodique :
Bulletin de la Société Royale des Sciences de Liège
ISSN :
0037-9565
eISSN :
1783-5720
Maison d'édition :
Societe Royale des Sciences de Liege
Volume/Tome :
93
Fascicule/Saison :
3
Pagination :
374 - 378
Peer review/Comité de sélection :
Editorial Reviewed vérifié par ORBi
Subventionnement (détails) :
I would like to thank Prof. Maxime Fays for the idea of the project and Prof. Jean-Rene Cudell for the discussions and suggestions on the topic. This work was supported by the Fonds de la Recherche Scientifique-FNRS, Belgium, under grant No. 4.4501. The author is grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459.
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