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Automatic Source Classification in Digitised First Byurakan Survey
Published online by Cambridge University Press: 30 May 2017
Abstract
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The Digitised First Byurakan Survey (DFBS) provides low dispersion optical spectra for about 24 million sources. A two-step machine learning algorithm based on similarities to predefined templates is applied to select different classes of rare objects in the dataset automatically, for example late type stars, quasars and white dwarves. Identifying outliers from the groups of common astrophysical objects may lead to discovery of rare objects, such as gamma-ray burst afterglows.
- Type
- Contributed Papers
- Information
- Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 186 - 190
- Copyright
- Copyright © International Astronomical Union 2017
References
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