http://arxiv.org/abs/1601.06151
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.
J. Klencki and L. Wyrzykowski
Mon, 25 Jan 16
19/56
Comments: to be published in the Proceedings of the XXXVII Meeting of the Polish Astronomical Society
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