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| Paper: |
Machine Learning Variability Classification in the OGLE Project |
| Volume: |
521, Astronomical Data Analysis Software and Systems XXVI |
| Page: |
319 |
| Authors: |
Pawlak, M. |
| Abstract: |
The OGLE project is one of the largest photometric variability surveys, regularly monitoring about one billion sources in the densest sky regions. The huge amount of data collected gives a unique possibility to detect and study different types of variables but on the other hand it makes the automatization of the classification process a must. A machine learning approach has been successfully applied to solve this problem, both in the case of transient events, as well as for periodic variables. The overview of the machine learning applications in the OGLE project and a detailed description of the latest result – the Random Forest classification of eclipsing binaries – is presented. |
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