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We present a spatio-temporal AI framework that concurrently exploits both thespatial and time-variable features of gravitationally lensed supernovae inoptical images to ultimately aid in future discoveries of such exotictransients in wide-field surveys. Our spatio-temporal engine is designed usingrecurrent convolutional layers, while drawing from recent advances invariational inference to quantify approximate Bayesian uncertainties via aconfidence score. Using simulated Young Supernova Experiment (YSE) images oflensed and non-lensed supernovae as a showcase, we find that the use oftime-series images adds relevant information from time variability of spatiallight distribution of partially blended images of lensed supernova, yielding asubstantial gain of around 20 per cent in classification accuracy oversingle-epoch observations. Preliminary application of our network to mockobservations from the Legacy Survey of Space and Time (LSST) results indetections with accuracy reaching around 99 percent. Our innovative deeplearning machinery is versatile and can be employed to search for any class ofsources which exhibit variability both in flux and spatial distribution oflight.

AI-driven spatio-temporal engine for finding gravitationally lensed type Ia supernovae