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There is a shortage of multi-wavelength and spectroscopic followupcapabilities given the number of transient and variable astrophysical eventsdiscovered through wide-field, optical surveys such as the upcoming Vera C.Rubin Observatory. From the haystack of potential science targets, astronomersmust allocate scarce resources to study a selection of needles in real time.Here we present a variational recurrent autoencoder neural network to encodesimulated Rubin Observatory extragalactic transient events using 1% of thePLAsTiCC dataset to train the autoencoder. Our unsupervised method uniquelyworks with unlabeled, real time, multivariate and aperiodic data. We rank1,129,184 events based on an anomaly score estimated using an isolation forest.We find that our pipeline successfully ranks rarer classes of transients asmore anomalous. Using simple cuts in anomaly score and uncertainty, we identifya pure (~95% pure) sample of rare transients (i.e., transients other than TypeIa, Type II and Type Ibc supernovae) including superluminous andpair-instability supernovae. Finally, our algorithm is able to identify thesetransients as anomalous well before peak, enabling real-time follow up studiesin the era of the Rubin Observatory.

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients