  There is a shortage of multi-wavelength and spectroscopic followup
capabilities given the number of transient and variable astrophysical events
discovered through wide-field, optical surveys such as the upcoming Vera C.
Rubin Observatory. 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 dataset 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.
