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Paper: Automatic Classification of Rare Sources in DFBS
Volume: 511, Non-Stable Universe: Energetic Resources, Activity Phenomena and Evolutionary Processes
Page: 164
Authors: Topinka, M.; Mickaelian, A. M.; Nesci, R.; Rossi, C.
Abstract: The Digitised First Byurakan Survey (DFBS) provides low-resolution dispersion optical spectra for more than 24 million objects. A two-step (rough filter and fine search) machine learning algorithm based on measuring similarities to predefined templates is applied to identify classes of template-like objects in the dataset. The templates include late type stars (carbon and M stars), quasars and white dwarfs. The method can be applied as a quick-look analysis in other sets of low resolution spectra, e.g. in the GAIA mission.
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