AI-driven spatio-temporal engine for finding gravitationally lensed type
  Ia supernovae
  We present a spatio-temporal AI framework that concurrently exploits both the
spatial and time-variable features of gravitationally lensed supernovae in
optical images to ultimately aid in future discoveries of such exotic
transients in wide-field surveys. Our spatio-temporal engine is designed using
recurrent convolutional layers, while drawing from recent advances in
variational inference to quantify approximate Bayesian uncertainties via a
confidence score. Using simulated Young Supernova Experiment (YSE) images of
lensed and non-lensed supernovae as a showcase, we find that the use of
time-series images adds relevant information from time variability of spatial
light distribution of partially blended images of lensed supernova, yielding a
substantial gain of around 20 per cent in classification accuracy over
single-epoch observations. Preliminary application of our network to mock
observations from the Legacy Survey of Space and Time (LSST) results in
detections with accuracy reaching around 99 percent. Our innovative deep
learning machinery is versatile and can be employed to search for any class of
sources which exhibit variability both in flux and spatial distribution of
light.
