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

Morgan, R.; Nord, B.; Bechtol, K.; Gonzalez, S. J.; Buckley-Geer, E.; Moller, A.; Park, J. W.; Kim, A. G.; Birrer, S.; Aguena, M.; Annis, J.; Bocquet, S.; Brooks, D.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Cawthon, R.; da Costa, L. N.; Davis, T. M.; De Vicente, J.; Doel, P.; Ferrero, I.; Friedel, D.; Frieman, J.; Garcia-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-Chinchon, F.; Pereira, M. E. S.; Pieres, A.; Malagon, A. A. Plazas; Reil, K.; Roodman, A.; Sanchez, E.; Smith, M.; Suchyta, E.; Swanson, M. E. C.; Tarle, G. und To, C. (2022): DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification. In: Astrophysical Journal, Bd. 927, Nr. 1, 109

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 530 Physik
• ISSN: 0004-637X
• Sprache: Englisch