1. DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning
- Author
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R. Morgan, B. Nord, K. Bechtol, A. Möller, W. G. Hartley, S. Birrer, S. J. González, M. Martinez, R. A. Gruendl, E. J. Buckley-Geer, A. J. Shajib, A. Carnero Rosell, C. Lidman, T. Collett, T. M. C. Abbott, M. Aguena, F. Andrade-Oliveira, J. Annis, D. Bacon, S. Bocquet, D. Brooks, D. L. Burke, M. Carrasco Kind, J. Carretero, F. J. Castander, C. Conselice, L. N. da Costa, M. Costanzi, J. De Vicente, S. Desai, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, E. Gaztanaga, D. Gruen, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, N. Kuropatkin, O. Lahav, M. Lima, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, M. E. S. Pereira, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, A. Roodman, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, D. Thomas, T. N. Varga, Morgan, R [0000-0002-7016-5471], Nord, B [0000-0001-6706-8972], Bechtol, K [0000-0001-8156-0429], Möller, A [0000-0001-8211-8608], Birrer, S [0000-0003-3195-5507], González, SJ [0000-0001-7282-3864], Martinez, M [0000-0002-8397-8412], Gruendl, RA [0000-0002-4588-6517], Buckley-Geer, EJ [0000-0002-3304-0733], Shajib, AJ [0000-0002-5558-888X], Rosell, A Carnero [0000-0003-3044-5150], Lidman, C [0000-0003-1731-0497], Collett, T [0000-0001-5564-3140], Aguena, M [0000-0001-5679-6747], Annis, J [0000-0002-0609-3987], Bacon, D [0000-0002-2562-8537], Bocquet, S [0000-0002-4900-805X], Brooks, D [0000-0002-8458-5047], Kind, M Carrasco [0000-0002-4802-3194], Carretero, J [0000-0002-3130-0204], Castander, FJ [0000-0001-7316-4573], Conselice, C [0000-0003-1949-7638], Costanzi, M [0000-0001-8158-1449], De Vicente, J [0000-0001-8318-6813], Desai, S [0000-0002-0466-3288], Flaugher, B [0000-0002-2367-5049], Frieman, J [0000-0003-4079-3263], García-Bellido, J [0000-0002-9370-8360], Gaztanaga, E [0000-0001-9632-0815], Gruen, D [0000-0003-3270-7644], Gutierrez, G [0000-0003-0825-0517], Hinton, SR [0000-0003-2071-9349], Hollowood, DL [0000-0002-9369-4157], Honscheid, K [0000-0002-6550-2023], Kuehn, K [0000-0003-0120-0808], Kuropatkin, N [0000-0003-2511-0946], Lahav, O [0000-0002-1134-9035], Menanteau, F [0000-0002-1372-2534], Miquel, R [0000-0002-6610-4836], Palmese, A [0000-0002-6011-0530], Paz-Chinchón, F [0000-0003-1339-2683], Pieres, A [0000-0001-9186-6042], Malagón, AA Plazas [0000-0002-2598-0514], Romer, AK [0000-0002-9328-879X], Roodman, A [0000-0001-5326-3486], Sanchez, E [0000-0002-9646-8198], Sevilla-Noarbe, I [0000-0002-1831-1953], Smith, M [0000-0002-3321-1432], Suchyta, E [0000-0002-7047-9358], Tarle, G [0000-0003-1704-0781], and Apollo - University of Cambridge Repository
- Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,FOS: Physical sciences ,Física ,Astronomy and Astrophysics ,5109 Space Sciences ,Basic Behavioral and Social Science ,Supernovae ,Space and Planetary Science ,5101 Astronomical Sciences ,Strong gravitational lensing ,Behavioral and Social Science ,7 Affordable and Clean Energy ,51 Physical Sciences ,5107 Particle and High Energy Physics ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM, 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, The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union.
- Published
- 2022