Real-time detection of transients in OGLE-IV with application of machine learning

Jakub Klencki and Lukasz Wyrzykowski

The current bottleneck of transient detection in most surveys is the problem of rejecting numerous artifacts from detected candidates. We present a triple-stage hierarchical machine learning system for automated artifact filtering in difference imaging, based on self-organizing maps. The classifier, when tested on the OGLE-IV Transient Detection System, accepts 97% of real transients while removing up to 97.5% of artifacts.

Proceedings of the Polish Astronomical Society, vol. 3, 56-58 (2016)

Download full article as PDF file: