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Abstrakty
Real-time analysis and classification of observational data collected within synoptic sky surveys is a huge challenge due to constant growth of data volumes. Machine learning techniques are often applied in order to perform this task automatically. The current bottleneck of transients detection in most surveys is the process of filtering numerous artifacts from candidate detection. We present a new method for automated artifact filtering based on hierarchical unsupervised classifier employing Self-Organizing Maps (SOMs). The system accepts 97% of real transients and removes 97.5% of artifacts when tested on the OGLE-IV Transient Detection System. The improvement of the artifacts filtering allows for single-frame-based detection of transients within OGLE-IV, which now alerts on transient discoveries in less than 15 minutes from the image acquisition.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
15--29
Opis fizyczny
Bibliogr. 41 poz., rys., wykr., zdj.
Twórcy
autor
- Warsaw University Observatory, Al. Ujazdowskie 4, 00-478 Warsaw, Poland
autor
- Warsaw University Observatory, Al. Ujazdowskie 4, 00-478 Warsaw, Poland
autor
- Warsaw University Observatory, Al. Ujazdowskie 4, 00-478 Warsaw, Poland
autor
- Warsaw University Observatory, Al. Ujazdowskie 4, 00-478 Warsaw, Poland
Bibliografia
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- Nicholl, M., et al. 2015a, ApJ, 807, L18.
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-916dea88-5418-44d0-b1da-4f71191a06b6