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Real-time analysis and classification of observational data collected withinsynoptic sky surveys is a huge challenge due to constant growth of datavolumes. Machine learning techniques are often applied in order to perform thistask automatically. The current bottleneck of transients detection in mostsurveys is the process of filtering numerous artifacts from candidatedetection. We present a new method for automated artifact filtering based onhierarchical unsupervised classifier employing Self-Organizing Maps (SOMs). Thesystem accepts 97 % of real transients and removes 97.5 % of artifacts whentested on the OGLE-IV Transient Detection System. The improvement of theartifacts filtering allowed for single-frame based rapid detections oftransients within OGLE-IV, which now alerts on transient discoveries in lessthan 15 minutes from the image acquisition.

Robust Filtering of Artifacts in Difference Imaging for Rapid Transients Detection