Machine Learning Methods used to Distinguish Open Clustersfrom Asterisms using Gaia DR3 data - Kapanadze, G.; Kvernadze, T.; Kvaratskhelia, O.; Kurkhuli, G.; Kozlov, V.
We present the use of the scikit-learn DBSCAN clustering code as a machine learning tool to test the membership and integrity of apparent open clusters to distinguish asterisms from real open clusters in the Gaia DR3 3D data space. For testing means, we studied known Open Clusters NGC 1798 and NGC 6633. In the field of NGC 1798 we accidentally confirmed an Open Cluster LP17. For final analyze, we processed the 11 open clusters of Dolidze-Jimsheleishvili as the most of them are small having low spatial density and are hard to confirm as an Open Clusters with other methods. As a result, we report that 3 of them show clustering tendency as the open clusters: DolidzeDzim 6, DolidzeDzim 7 and DolidzeDzim 10.
Keywords: Astrophysics