DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

Morgan, R.; Nord, B.; Bechtol, K.; Möller, A.; Hartley, W. G.; Birrer, S.; González, S. J.; Martinez, M.; Gruendl, R. A.; Buckley-Geer, E. J.; Shajib, A. J.; Carnero Rosell, A.; Lidman, C.; Collett, T.; Abbott, T. M. C.; Aguena, M.; Andrade-Oliveira, F.; Annis, J.; Bacon, D.; Bocquet, S.; Brooks, D.; Burke, D. L.; Carrasco Kind, M.; Carretero, J.; Castander, F. J.; Conselice, C.; da Costa, L. N.; Costanzi, M.; De Vicente, J.; Desai, S.; Doel, P.; Everett, S.; Ferrero, I.; Flaugher, B.; Friedel, D.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Gruen, D.; Gutierrez, G.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lima, M.; Menanteau, F.; Miquel, R.; Palmese, A.; Paz-Chinchón, F.; Pereira, M. E. S.; Pieres, A.; Plazas Malagón, A. A.; Prat, J.; Rodriguez-Monroy, M.; Romer, A. K.; Roodman, A.; Sanchez, E.; Scarpine, V.; Sevilla-Noarbe, I.; Smith, M.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Thomas, D.; Varga, T. N.
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The Astrophysical Journal

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Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain 5-10 LSNe in total while next-generation experiments are expected to contain several hundred to a few thousand of these systems. We search for these systems in observed Dark Energy Survey (DES) five year SN fields-10 3 sq. deg. regions of sky imaged in the griz bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains an LSN recall of 61.13% and a false-positive rate of 0.02% on the DES SN field data. DeepZipper selected 2245 candidates from a magnitude-limited (m i < 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields.