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