DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification

R. Morgan, B. Nord, K. Bechtol, S. J. González, E. Buckley-Geer, A. Möller, J. W. Park, A. G. Kim, S. Birrer, M. Aguena, J. Annis, S. Bocquet, D. Brooks, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, R. Cawthon, L. N. Da Costa, T. M. Davis, J. De VicenteP. Doel, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, D. Gruen, R. A. Gruendl, G. Gutierrez, D. L. Hollowood, K. Honscheid, D. J. James, K. Kuehn, N. Kuropatkin, M. A.G. Maia, R. Miquel, A. Palmese, F. Paz-Chinchón, M. E.S. Pereira, A. Pieres, A. A. Plazas Malagón, K. Reil, A. Roodman, E. Sanchez, M. Smith, E. Suchyta, M. E.C. Swanson, G. Tarle, C. To

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number109
JournalAstrophysical Journal
Issue number1
StatePublished - Mar 1 2022

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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