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| Título: | DeepZipper: A novel deep-learning architecture for lensed supernovae identification |
Autor: | Morgan, Robert A.; García-Bellido, Juan CSIC ORCID; Gaztañaga, Enrique CSIC ORCID | Fecha de publicación: | 2022 | Editor: | IOP Publishing | Citación: | Astrophysical Journal 927(1): 109 (2022) | Resumen: | 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. | Descripción: | Morgan et al. | Versión del editor: | https://doi.org/10.3847/1538-4357/ac5178 | URI: | http://hdl.handle.net/10261/296712 | DOI: | 10.3847/1538-4357/ac5178 | E-ISSN: | 1538-4357 |
| Aparece en las colecciones: | (ICE) Artículos (IFT) Artículos |
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| deepidenti.pdf | 3,82 MB | Adobe PDF |  Visualizar/Abrir |
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