DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification
dc.date.accessioned | 2022-06-29T17:02:34Z | |
dc.date.available | 2022-06-29T17:02:34Z | |
dc.date.created | 2022-06-07T08:32:47Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Morgan, R. Nord, B. Bechtol, K. González, S.J. Buckley-Geer, E. Möller, A. Park, J.W. Kim, A.G. Birrer, S. Aguena, M. Annis, J. Bocquet, S. Brooks, D. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Cawthon, R. Da Costa, L.N. Davis, T.M. De Vicente, J. Doel, P. Ferrero, Ismael Friedel, D. Frieman, J. García-Bellido, J. Gatti, M. Gaztanaga, E. Giannini, G. Gruen, D. Gruendl, R.A. Gutierrez, G. Hollowood, D.L. Honscheid, K. James, D.J. Kuehn, K. Kuropatkin, N. Maia, M.A.G. Miquel, R. Palmese, A. Paz-Chinchón, F. Pereira, M.E.S. Pieres, A. Plazas Malagón, A.A. Reil, K. Roodman, A. Sanchez, E. Smith, M. Suchyta, E. Swanson, M.E.C. Tarle, G. To, C. . DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification. The Astrophysical Journal (ApJ). 2022, 927(1) | |
dc.identifier.uri | http://hdl.handle.net/10852/94520 | |
dc.description.abstract | 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. | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification | |
dc.title.alternative | ENEngelskEnglishDeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification | |
dc.type | Journal article | |
dc.creator.author | Morgan, R. | |
dc.creator.author | Nord, B. | |
dc.creator.author | Bechtol, K. | |
dc.creator.author | González, S.J. | |
dc.creator.author | Buckley-Geer, E. | |
dc.creator.author | Möller, A. | |
dc.creator.author | Park, J.W. | |
dc.creator.author | Kim, A.G. | |
dc.creator.author | Birrer, S. | |
dc.creator.author | Aguena, M. | |
dc.creator.author | Annis, J. | |
dc.creator.author | Bocquet, S. | |
dc.creator.author | Brooks, D. | |
dc.creator.author | Carnero Rosell, A. | |
dc.creator.author | Carrasco Kind, M. | |
dc.creator.author | Carretero, J. | |
dc.creator.author | Cawthon, R. | |
dc.creator.author | Da Costa, L.N. | |
dc.creator.author | Davis, T.M. | |
dc.creator.author | De Vicente, J. | |
dc.creator.author | Doel, P. | |
dc.creator.author | Ferrero, Ismael | |
dc.creator.author | Friedel, D. | |
dc.creator.author | Frieman, J. | |
dc.creator.author | García-Bellido, J. | |
dc.creator.author | Gatti, M. | |
dc.creator.author | Gaztanaga, E. | |
dc.creator.author | Giannini, G. | |
dc.creator.author | Gruen, D. | |
dc.creator.author | Gruendl, R.A. | |
dc.creator.author | Gutierrez, G. | |
dc.creator.author | Hollowood, D.L. | |
dc.creator.author | Honscheid, K. | |
dc.creator.author | James, D.J. | |
dc.creator.author | Kuehn, K. | |
dc.creator.author | Kuropatkin, N. | |
dc.creator.author | Maia, M.A.G. | |
dc.creator.author | Miquel, R. | |
dc.creator.author | Palmese, A. | |
dc.creator.author | Paz-Chinchón, F. | |
dc.creator.author | Pereira, M.E.S. | |
dc.creator.author | Pieres, A. | |
dc.creator.author | Plazas Malagón, A.A. | |
dc.creator.author | Reil, K. | |
dc.creator.author | Roodman, A. | |
dc.creator.author | Sanchez, E. | |
dc.creator.author | Smith, M. | |
dc.creator.author | Suchyta, E. | |
dc.creator.author | Swanson, M.E.C. | |
dc.creator.author | Tarle, G. | |
dc.creator.author | To, C. | |
cristin.unitcode | 185,15,3,0 | |
cristin.unitname | Institutt for teoretisk astrofysikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 2029721 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=The Astrophysical Journal (ApJ)&rft.volume=927&rft.spage=&rft.date=2022 | |
dc.identifier.jtitle | The Astrophysical Journal (ApJ) | |
dc.identifier.volume | 927 | |
dc.identifier.issue | 1 | |
dc.identifier.pagecount | 12 | |
dc.identifier.doi | https://doi.org/10.3847/1538-4357/ac5178 | |
dc.identifier.urn | URN:NBN:no-97059 | |
dc.subject.nvi | VDP::Astrofysikk, astronomi: 438 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0004-637X | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/94520/1/Morgan_2022_ApJ_927_109.pdf | |
dc.type.version | PublishedVersion | |
cristin.articleid | 109 | |
dc.relation.project | NFR/287772 |
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