  Real-time analysis and classification of observational data collected within
synoptic sky surveys is a huge challenge due to constant growth of data
volumes. Machine learning techniques are often applied in order to perform this
task automatically. The current bottleneck of transients detection in most
surveys is the process of filtering numerous artifacts from candidate
detection. We present a new method for automated artifact filtering based on
hierarchical unsupervised classifier employing Self-Organizing Maps (SOMs). The
system accepts 97 % of real transients and removes 97.5 % of artifacts when
tested on the OGLE-IV Transient Detection System. The improvement of the
artifacts filtering allowed for single-frame based rapid detections of
transients within OGLE-IV, which now alerts on transient discoveries in less
than 15 minutes from the image acquisition.
