  Supernovae mark the explosive deaths of stars and enrich the cosmos with
heavy elements. Future telescopes will discover thousands of new supernovae
nightly, creating a need to flag astrophysically interesting events rapidly for
followup study. Ideally, such an anomaly detection pipeline would be
independent of our current knowledge and be sensitive to unexpected phenomena.
Here we present an unsupervised method to search for anomalous time series in
real time for transient, multivariate, and aperiodic signals. We use a
RNN-based variational autoencoder to encode supernova time series and an
isolation forest to search for anomalous events in the learned encoded space.
We apply this method to a simulated dataset of 12,159 supernovae, successfully
discovering anomalous supernovae and objects with catastrophically incorrect
redshift measurements. This work is the first anomaly detection pipeline for
supernovae which works with online datastreams.
