Expediting DECam multimessenger counterpart searches with convolutional neural networks
Entity
UAM. Departamento de Física TeóricaPublisher
American Astronomical SocietyDate
2022-01-24Citation
10.3847/1538-4357/ac3760
Astrophysical Journal 925.1 (2022): 44
ISSN
0004-637X (print); 1538-4357 (online)DOI
10.3847/1538-4357/ac3760Project
Gobierno de España. ESP2017-89838; Gobierno de España. PGC2018-094773; Gobierno de España. PGC2018-102021; Gobierno de España. SEV-2016-0588; Gobierno de España. SEV-2016-0597; Gobierno de España. MDM-2015-0509; info:eu-repo/grantAgreement/EC/FP7/240672; info:eu-repo/grantAgreement/EC/FP7/291329; info:eu-repo/grantAgreement/EC/FP7/306478Editor's Version
https://doi.org/10.3847/1538-4357/ac3760Subjects
Neutrinos; Neutrino detectors; Gravitational wave; DECam; Dark energy survey gravitational wave; FísicaRights
© 2022. The Author(s)Abstract
Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact-detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor-quality image subtractions, false detections from low signal-to-noise images, and detections of preexisting variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92% on our data set. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community
Files in this item
Google Scholar:Shandonay, A.
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Morgan, R.
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Bechtol, K.
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Bom, C. R.
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Nord, B.
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Garcia, A.
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Henghes, B.
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Herner, K.
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Tabbutt, M.
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Palmese, A.
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Santana-Silva, L.
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Soares-Santos, M.
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Gill, M. S.S.
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García-Bellido Capdevila, Juan
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