A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients

Abstract

There is a shortage of multiwavelength and spectroscopic follow-up capabilities given the number of transient and variable astrophysical events discovered through wide-field optical surveys such as the upcoming Vera C. Rubin Observatory and its associated Legacy Survey of Space and Time. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC data set to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real-time, multivariate, and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (≈95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II, and Type Ibc supernovae), including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow-up studies in the era of the Rubin Observatory.