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arXiv · 1601.06151

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

Jakub Klencki·Łukasz Wyrzykowski
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Abstract

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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Jakub Klencki, Łukasz Wyrzykowski. 2016-01-22. Real-time detection of transients in OGLE-IV with application of machine learning. https://arxiv.org/abs/1601.06151

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