apjThe Astrophysical JournalAPJAstrophys. J.0004-637X1538-4357The American Astronomical Societyapjac517810.3847/1538-4357/ac5178ac5178AAS36365310Galaxies and CosmologyDeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification0000-0002-7016-5471MorganR.123robert.morgan@wisc.edu0000-0001-6706-8972NordB.2450000-0001-8156-0429BechtolK.160000-0001-7282-3864GonzálezS. J.10000-0002-3304-0733Buckley-GeerE.240000-0001-8211-8608MöllerA.70000-0002-0692-1092ParkJ. W.89KimA. G.100000-0003-3195-5507BirrerS.890000-0001-5679-6747AguenaM.110000-0002-0609-3987AnnisJ.20000-0002-4900-805XBocquetS.120000-0002-8458-5047BrooksD.130000-0003-3044-5150Carnero RosellA.110000-0002-4802-3194Carrasco KindM.14150000-0002-3130-0204CarreteroJ.160000-0003-2965-6786CawthonR.170000-0002-7731-277Xda CostaL. N.11180000-0002-4213-8783DavisT. M.190000-0001-8318-6813De VicenteJ.20DoelP.13FerreroI.210000-0002-3632-7668FriedelD.140000-0003-4079-3263FriemanJ.250000-0002-9370-8360García-BellidoJ.22GattiM.230000-0001-9632-0815GaztanagaE.24250000-0002-3730-1750GianniniG.160000-0003-3270-7644GruenD.120000-0002-4588-6517GruendlR. A.14150000-0003-0825-0517GutierrezG.20000-0002-9369-4157HollowoodD. L.260000-0002-6550-2023HonscheidK.27280000-0001-5160-4486JamesD. J.290000-0003-0120-0808KuehnK.30310000-0003-2511-0946KuropatkinN.20000-0001-9856-9307MaiaM. A. G.11180000-0002-6610-4836MiquelR.16320000-0002-6011-0530PalmeseA.330000-0003-1339-2683Paz-ChinchónF.1434PereiraM. E. S.35360000-0001-9186-6042PieresA.11180000-0002-2598-0514Plazas MalagónA. A.37ReilK.90000-0001-5326-3486RoodmanA.9380000-0002-9646-8198SanchezE.200000-0002-3321-1432SmithM.390000-0002-7047-9358SuchytaE.40SwansonM. E. C.390000-0003-1704-0781TarleG.350000-0001-7836-2261ToC.27 Physics Department, University of Wisconsin-Madison, 1150 University Avenue Madison, WI 53706, USA; robert.morgan@wisc.edu Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, IL 60510, USA Legacy Survey of Space and Time Corporation Data Science Fellowship Program, USA Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637, USA Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA Legacy Survey of Space and Time, 933 North Cherry Avenue, Tucson, AZ 85721, USA Centre for Astrophysics & Supercomputing, Swinburne University of Technology, Victoria 3122, Australia Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics, Stanford University, Stanford, CA 94305, USA SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA Laboratório Interinstitucional de e-Astronomia—LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ—20921-400, Brazil Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstraße 1, D-81679 Munich, Germany Department of Physics & Astronomy, University College London, Gower Street, London, WC1E 6BT, UK Center for Astrophysical Surveys, National Center for Supercomputing Applications, 1205 West Clark Strett, Urbana, IL 61801, USA Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 West Green Street, Urbana, IL 61801, USA Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, E-08193 Bellaterra (Barcelona) Spain Physics Department, William Jewell College, Liberty, MO 64068 USA Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ—20921-400, Brazil School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, NO-0315 Oslo, Norway Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, E-28049 Madrid, Spain Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA Institut d’Estudis Espacials de Catalunya (IEEC), E-08034 Barcelona, Spain Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, E-08193 Barcelona, Spain Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA Department of Physics, The Ohio State University, Columbus, OH 43210, USA Center for Astrophysics ∣ Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA Australian Astronomical Optics, Macquarie University, North Ryde, NSW 2113, Australia Lowell Observatory, 1400 Mars Hill Road, Flagstaff, AZ 86001, USA Institució Catalana de Recerca i Estudis Avançats, E-08010 Barcelona, Spain Department of Astronomy, University of California, Berkeley, 501 Campbell Hall, Berkeley, CA 94720, USA Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, D-21029 Hamburg, Germany Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA Kavli Institute for Particle Astrophysics & Cosmology, Stanford University, P.O. Box 2450, Stanford, CA 94305, USA School of Physics and Astronomy, University of Southampton, Southampton, SO17 1BJ, UK Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA 0132022090320220903202292711092122021271202212202201022022© 2022. The Author(s). Published by the American Astronomical Society.2022 Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.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.

unified-astronomy-thesaurushttp://astrothesaurus.org/uat/1668Supernovae1668http://astrothesaurus.org/uat/1643Strong gravitational lensing1643http://astrothesaurus.org/uat/1933Neural networks1933ccc0004-637X/22/109+12$33.00crossmarkyes