DeepZipper: A novel deep-learning architecture for lensed supernovae identification
Entidad
UAM. Departamento de Física TeóricaEditor
American Astronomical SocietyFecha de edición
2022-03-01Cita
10.3847/1538-4357/ac5178
Astrophysical Journal 927.1 (2022): 109
ISSN
0004-637X (print); 1538-4357 (online)DOI
10.3847/1538-4357/ac5178Proyecto
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/EU//COGS; info:eu-repo/grantAgreement/EC/FP7/291329/EU//TESTDE; info:eu-repo/grantAgreement/EC/FP7/306478/EU//COSMICDAWNVersión del editor
https://doi.org/10.3847/1538-4357/ac5178Materias
Lens; Quasars; Galaxies; FísicaNota
Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAMDerechos
© 2022 The Author(s)Resumen
Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey data sets, we designed ZipperNet, a multibranch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories - no lens, galaxy-galaxy lens, lensed Type-Ia supernova, lensed core-collapse supernova - within high-fidelity simulations of three cosmic survey data sets: the Dark Energy Survey, Rubin Observatory's Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like data set, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1-2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments. © 2022. The Author(s). Published by the American Astronomical Society
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Google Scholar:Morgan, R.
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García-Bellido Capdevila, Juan
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