Sunday, April 17, 2016

Using Machine Learning for Real-time Detection of Transiets in OGLE-IV

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


Klencki et al


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.

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