@misc{Noormohammadi_M._Membership,
 author={Noormohammadi, M. and Javadi, Atefeh},
 howpublished={online},
 abstract={The first and most important stage in studying open clusters is the detection of reliable members. Since open clusters form and evolve within the inner disk of the galaxy, they are surrounded by numerous field stars, making membership determination challenging. Because cluster members originate from the same molecular clouds, they exhibit similar physical parameters—such as proper motion and parallax—and align along a single main sequence in the color-magnitude diagram. For this reason, machine learning algorithms can identify cluster members as familiar data among field stars. In this work, we used a combination of unsupervised machine learning algorithms—DBSCAN and GMM—based on astrometric parameters, proper motion, parallax, and position from the latest Gaia data release (GDR3). After selecting reliable members within the tidal radius, we applied the Random Forest algorithm to detect members beyond the tidal radius, utilizing proper motion, parallax, G-band magnitude, and BP-RP color index as classification features. By leveraging accurate data and a suitable method capable of handling large datasets, we identified members both inside and beyond the tidal radius of clusters. We observed clusters with a comprehensive field of view and analyzed their morphology. All members outside the tidal radius fall within the range of proper motion, parallax, and the main sequence of members inside the tidal radius.},
 title={Membership Analysis of Open Clusters Using Machine Learning on Gaia Data Release 3},
 type={Electronic journal},
 keywords={Astrometry, Space Sciences, Archaeoastronomy and Astronomy in Culture},
}