DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey
  Data with Deep Learning
  Gravitationally lensed supernovae (LSNe) are important probes of cosmic
expansion, but they remain rare and difficult to find. Current cosmic surveys
likely contain and 5-10 LSNe in total while next-generation experiments are
expected to contain several hundreds to a few thousands of these systems. We
search for these systems in observed Dark Energy Survey (DES) 5-year SN fields
-- 10 3-sq. deg. regions of sky imaged in the $griz$ bands approximately every
six nights over five years. To perform the search, we utilize the DeepZipper
approach: a multi-branch deep learning architecture trained on image-level
simulations of LSNe that simultaneously learns spatial and temporal
relationships from time series of images. We find that our method obtains a LSN
recall of 61.13% and a false positive rate of 0.02% on the DES SN field data.
DeepZipper selected 2,245 candidates from a magnitude-limited ($m_i$ $<$ 22.5)
catalog of 3,459,186 systems. We employ human visual inspection to review
systems selected by the network and find three candidate LSNe in the DES SN
fields.
