DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification
Authors
Kim, AG
Doel, P
Ferrero, I
Gatti, M
Pereira, MES
Reil, K
Swanson, MEC
Publication Date
2022-03-01Journal Title
Astrophysical Journal
ISSN
0004-637X
Publisher
American Astronomical Society
Volume
927
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Morgan, R., Nord, B., Bechtol, K., González, S., Buckley-Geer, E., Möller, A., Park, J., et al. (2022). DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification. Astrophysical Journal, 927 (1) https://doi.org/10.3847/1538-4357/ac5178
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 datasets, we designed ZipperNet, a multi-branch deep neural
network that combines convolutional layers (traditionally used for images) with
long short-term memory (LSTM) 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 (DES), 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 dataset, 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.
Keywords
310, Galaxies and Cosmology
Identifiers
apjac5178, ac5178, aas36365
External DOI: https://doi.org/10.3847/1538-4357/ac5178
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334817
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.