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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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