  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.
