학술논문
DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification
Document Type
article
Author
Morgan, R; Nord, B; Bechtol, K; González, SJ; Buckley-Geer, E; Möller, A; Park, JW; Kim, AG; Birrer, S; Aguena, M; Annis, J; Bocquet, S; Brooks, D; Rosell, A Carnero; Kind, M Carrasco; Carretero, J; Cawthon, R; da Costa, LN; Davis, TM; De Vicente, J; Doel, P; Ferrero, I; Friedel, D; Frieman, J; García-Bellido, J; Gatti, M; Gaztanaga, E; Giannini, G; Gruen, D; Gruendl, RA; Gutierrez, G; Hollowood, DL; Honscheid, K; James, DJ; Kuehn, K; Kuropatkin, N; Maia, MAG; Miquel, R; Palmese, A; Paz-Chinchón, F; Pereira, MES; Pieres, A; Malagón, AA Plazas; Reil, K; Roodman, A; Sanchez, E; Smith, M; Suchyta, E; Swanson, MEC; Tarle, G; To, C
Source
The Astrophysical Journal. 927(1)
Subject
Language
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.