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

Morgan, R. ; Nord, B. ; Bechtol, K. ; González, S. J. ; Buckley-Geer, E. ; Möller, A. ; Park, J. W. ; Kim, A. G. ; Birrer, S. ; Aguena, M. ; Annis, J. ; Bocquet, S. ; Brooks, D. ; Carnero Rosell, A. ; Carrasco Kind, M. ; Carretero, J. ; Cawthon, R. ; da Costa, L. N. ; Davis, T. M. ; De Vicente, J. ; Doel, P. ; Ferrero, I. ; Friedel, D. ; Frieman, J. ; García-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-Chinchón, F. ; Pereira, M. E. S. ; Pieres, A. ; Plazas Malagón, A. A. ; Reil, K. ; Roodman, A. ; Sanchez, E. ; Smith, M. ; Suchyta, E. ; Swanson, M. E. C. ; Tarle, G. ; To, C.

The Astrophysical journal, 2022-03, Vol.927 (1), p.109 [Periódico revisado por pares]

Philadelphia: The American Astronomical Society

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Título:
DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification
Autor: Morgan, R. ; Nord, B. ; Bechtol, K. ; González, S. J. ; Buckley-Geer, E. ; Möller, A. ; Park, J. W. ; Kim, A. G. ; Birrer, S. ; Aguena, M. ; Annis, J. ; Bocquet, S. ; Brooks, D. ; Carnero Rosell, A. ; Carrasco Kind, M. ; Carretero, J. ; Cawthon, R. ; da Costa, L. N. ; Davis, T. M. ; De Vicente, J. ; Doel, P. ; Ferrero, I. ; Friedel, D. ; Frieman, J. ; García-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-Chinchón, F. ; Pereira, M. E. S. ; Pieres, A. ; Plazas Malagón, A. A. ; Reil, K. ; Roodman, A. ; Sanchez, E. ; Smith, M. ; Suchyta, E. ; Swanson, M. E. C. ; Tarle, G. ; To, C.
Assuntos: Artificial neural networks ; ASTRONOMY AND ASTROPHYSICS ; Astrophysics ; Celestial bodies ; Dark energy ; Datasets ; Galaxies ; INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY ; Lenses ; Machine learning ; Neural networks ; Sky surveys (astronomy) ; Strong gravitational lensing ; Supernova ; Supernovae
É parte de: The Astrophysical journal, 2022-03, Vol.927 (1), p.109
Notas: Galaxies and Cosmology
AAS36365
NFR/287772
FERMILAB-PUB-21-392-E-SCD; arXiv:2112.01541
USDOE Office of Science (SC), High Energy Physics (HEP)
AC02-07CH11359; AC02-05CH11231; AC02-76SF00515; AC05-00OR22725
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Descrição: 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.
Editor: Philadelphia: The American Astronomical Society
Idioma: Inglês;Norueguês

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

Philadelphia: The American Astronomical Society

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