• Corpus ID: 118650251

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

@article{Klencki2016RealtimeDO,
  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},
  year={2016}
}
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. 

Figures from this paper

References

SHOWING 1-5 OF 5 REFERENCES
Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era
TLDR
The inner workings of a framework, based on machine-learning algorithms, that captures expert training and ground-truth knowledge about the variable and transient sky to automate the process of discovery on image differences, and the generation of preliminary science-type classifications of discovered sources are presented.
OGLE-IV Real-Time Transient Search
We present the design and first results of a real-time search for transients within the 650 sq. deg. area around the Magellanic Clouds, conducted as part of the OGLE-IV project and aimed at detecting
Data Mining and Machine-Learning in Time-Domain Discovery & Classification
TLDR
The emerging reliance on computation and machine learning is a general one, but the time-domain aspect of the data and the objects of interest presents some unique challenges, which are described and explored in this chapter.
OGLE-IV: Fourth Phase of the Optical Gravitational Lensing Experiment
We present both the technical overview and main science drivers of the fourth phase of the Optical Gravitational Lensing Experiment (hereafter OGLE-IV). OGLE-IV is currently one of the largest sky
Self-organized formation of topologically correct feature maps
TLDR
In a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events.