期刊
ASTROPHYSICAL JOURNAL
卷 927, 期 1, 页码 -出版社
IOP Publishing Ltd
DOI: 10.3847/1538-4357/ac5178
关键词
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资金
- Universities Research Association Fermilab Visiting Scholars Program
- LSSTC Data Science Fellowship Program - LSSTC, NSF [1829740]
- Brinson Foundation
- Moore Foundation
- National Science Foundation [1744555, AST1138766, AST-1536171]
- U.S. Department of Energy
- U.S. National Science Foundation
- Ministry of Science and Education of Spain
- Science and Technology Facilities Council of the United Kingdom
- Higher Education Funding Council for England
- National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
- Kavli Institute of Cosmological Physics at the University of Chicago
- Center for Cosmology and Astro-Particle Physics at the Ohio State University
- Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University
- Financiadora de Estudos e Projetos
- Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
- Ministerio da Ciencia, Tecnologia e Inovacao
- Deutsche Forschungsgemeinschaft
- Argonne National Laboratory
- University of California at Santa Cruz
- University of Cambridge
- Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid
- University of Chicago
- University College London
- DES-Brazil Consortium
- University of Edinburgh
- Eidgenossische Technische Hochschule (ETH) Zurich
- Fermi National Accelerator Laboratory
- University of Illinois at Urbana-Champaign
- Institut de Ciencies de l'Espai (IEEC/CSIC)
- Institut de Fisica d'Altes Energies
- Lawrence Berkeley National Laboratory
- Ludwig-Maximilians Universitat Munchen
- associated Excellence Cluster Universe
- University of Michigan
- NSF's NOIRLab
- University of Nottingham
- Ohio State University
- University of Pennsylvania
- University of Portsmouth
- SLAC National Accelerator Laboratory
- Stanford University
- University of Sussex
- Texas AM University
- OzDES Membership Consortium
- MICINN [ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509]
- ERDF funds from the European Union
- CERCA program of the Generalitat de Catalunya
- European Research Council under the European Union, ERC [240672, 291329, 306478]
- Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) e-Universe (CNPq) [465376/2014-2]
- U.S. Department of Energy, Office of Science, Office of High Energy Physics [DE-AC02-07CH11359]
- Direct For Education and Human Resources
- Division Of Graduate Education [1744555] Funding Source: National Science Foundation
Researchers have designed a multibranch deep neural network called ZipperNet that combines convolutional layers with long short-term memory layers to quickly identify strongly gravitationally lensed supernovae in optical survey data and predict their spectroscopic types.
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.
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