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
2022Journal 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.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk