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.

通过 RNN-based 变分自编码器和孤立森林算法，本研究提出了第一个用于超新星的异常检测管道，可以在在线数据流中快速检测到突发的，多变的和非周期性的异常事件。