• Corpus ID: 118650251

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

  title={Real-time detection of transients in OGLE-IV with application of machine learning},
  author={Jakub Klencki and Lukasz Wyrzykowski},
  journal={arXiv: Instrumentation and Methods for Astrophysics},
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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