Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87720
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Campo DC | Valor | Idioma |
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dc.contributor.author | Aveiro, João | por |
dc.contributor.author | Freitas, Felipe F. | por |
dc.contributor.author | Ferreira, Márcio | por |
dc.contributor.author | Onofre, A. | por |
dc.contributor.author | Providencia, Constança | por |
dc.contributor.author | Goncalves, Gonçalo | por |
dc.contributor.author | Font, José A. | por |
dc.date.accessioned | 2024-01-02T21:37:46Z | - |
dc.date.available | 2024-01-02T21:37:46Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Aveiro, J., Freitas, F. F., Ferreira, M., Onofre, A., Providência, C., Gonçalves, G., & Font, J. A. (2022, October 28). Identification of binary neutron star mergers in gravitational-wave data using object-detection machine learning models. Physical Review D. American Physical Society (APS). http://doi.org/10.1103/physrevd.106.084059 | por |
dc.identifier.issn | 2470-0010 | - |
dc.identifier.uri | https://hdl.handle.net/1822/87720 | - |
dc.description.abstract | We demonstrate the application of the YOLOv5 model, a general purpose convolution-based object detection model, in the task of detecting binary neutron star coalescence events from gravitational-wave data of current generation interferometer detectors. We also present a thorough explanation of the synthetic data generation and preparation tasks based on approximant waveform models used for the model training, validation and testing steps. Using this approach, we achieve mean average precision [0.50] values of 0.945 for a single class validation dataset and as high as 0.978 for test datasets. Moreover, the trained model is successful in identifying the GW170817 event in the LIGO H1 detector data. The identification of this event is also possible for the LIGO L1 detector data with an additional preprocessing step, without the need of removing the large glitch in the final stages of the inspiral. The detection of the GW190425 event is less successful, which attests to performance degradation with the signal-to-noise ratio. Our study indicates that the YOLOv5 model is an interesting approach for first-stage detection alarm pipelines. | por |
dc.description.sponsorship | J. A. acknowledges support by the project IMFire- Intelligent Management of Wildfires, ref. PCIF/SSI/0151/2018, and was fully funded by national funds through the Ministry of Science, Technology, and Higher Education. F. F. F. is supported by the FCT Project No. PTDC/FIS-PAR/31000/2017 and by the Center for Research and Development in Mathematics and Applications (CIDMA) through FCT, Grants No. UIDB/04106/2020 and No. UIDP/04106/2020. M. F. and C. P. acknowledge partial support by national funds from FCT (Fundacao para a Ciencia e a Tecnologia, I.P, Portugal) under the Projects No. UIDP/-04564/-2020 and No. UIDB/-04564/-2020. J. A. F. acknowledges support from the Spanish Agencia Estatal de Investigacion (Grants No. PGC2018-095984-B-I00 and No. PID2021-125485NB-C21) and from the Generalitat Valenciana (PROMETEO/2019/071). A. O. acknowledges support from national funds from FCT, under the Projects No. CERN/FIS-PAR/0029/2019 and CERN/FIS-PAR/0037/2021. The authors acknowledge the Laboratory for Advanced Computing at the University of Coimbra (http://www.uc.pt/lca) for providing access to the HPC computing resource Navigator, Minho Advanced Computing Center (MACC) for providing HPC resources that have contributed to the research results reported within this paper, the Portuguese National Network for Advanced Computing for the Grant No. CPCA/A1-428291-2021. Finally, the authors gratefully acknowledge the computer resources at Artemisa, funded by the European Union ERDF and Comunitat Valenciana as well as the technical support provided by the Instituto de Fisica Corpuscular, IFIC (CSIC-UV). | por |
dc.language.iso | eng | por |
dc.publisher | American Physical Society | por |
dc.relation | info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FFIS-PAR%2F31000%2F2017/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04106%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04106%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04564%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04564%2F2020/PT | por |
dc.relation | info:eu-repo/grantAgreement/FCT/3599-PPCDT/CERN%2FFIS-PAR%2F0029%2F2019/PT | por |
dc.relation | CERN/FIS-PAR/0037/2021 | por |
dc.rights | openAccess | por |
dc.title | Identification of binary neutron star mergers in gravitational-wave data using object-detection machine learning models | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://journals.aps.org/prd/abstract/10.1103/PhysRevD.106.084059 | por |
oaire.citationIssue | 8 | por |
oaire.citationVolume | 106 | por |
dc.identifier.doi | 10.1103/PhysRevD.106.084059 | por |
dc.subject.fos | Ciências Naturais::Ciências Físicas | por |
dc.description.publicationversion | info:eu-repo/semantics/publishedVersion | - |
dc.subject.wos | Science & Technology | por |
sdum.journal | Physical Review D - Particles, Fields, Gravitation and Cosmology | por |
oaire.version | AM | por |
Aparece nas coleções: | LIP - Artigos/papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2207.00591.pdf | 2,29 MB | Adobe PDF | Ver/Abrir |