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In the upcoming decade large astronomical surveys will discover millions oftransients raising unprecedented data challenges in the process. Only the useof the machine learning algorithms can process such large data volumes. Most ofthe discovered transients will belong to the known classes of astronomicalobjects. However, it is expected that some transients will be rare orcompletely new events of unknown physical nature. The task of finding them canbe framed as an anomaly detection problem. In this work, we perform for thefirst time an automated anomaly detection analysis in the photometric data ofthe Open Supernova Catalog (OSC), which serves as a proof of concept for theapplicability of these methods to future large scale surveys. The analysisconsists of the following steps: 1) data selection from the OSC andapproximation of the pre-processed data with Gaussian processes, 2)dimensionality reduction, 3) searching for outliers with the use of theisolation forest algorithm, 4) expert analysis of the identified outliers. Thepipeline returned 81 candidate anomalies, 27 (33%) of which were confirmed tobe from astrophysically peculiar objects. Found anomalies correspond to aselected sample of 1.4% of the initial automatically identified data sample of~2000 objects. Among the identified outliers we recognised superluminoussupernovae, non-classical Type Ia supernovae, unusual Type II supernovae, oneactive galactic nucleus and one binary microlensing event. We also found that16 anomalies classified as supernovae in the literature are likely to bequasars or stars. Our proposed pipeline represents an effective strategy toguarantee we shall not overlook exciting new science hidden in the data wefought so hard to acquire. All code and products of this investigation are madepublicly available.

Anomaly Detection in the Open Supernova Catalog