Bayesian Inference of Kinematics and Mass Segregation of Open Cluster. Issue Volume 12:Issue S316(2015) (31st March 2017)

• Record Type: Journal Article
• Authors: Shao, Z.; Xie, X.; Chen, L.; Zhong, J.; Hou, J.; Lin, C-C.
• Editors: Charbonnel, Corinne; Nota, Antonella
• Is Part Of: Proceedings of the International Astronomical Union. Volume 12:Issue S316(2015)
• Page Start: 265
• Page End: 266
• Publication Date: 2017-03-31
• Subjects: open cluster, -- mass segregation, -- bayesian inference, -- mixture model; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921315009011
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: Based on the Bayesian Inference (BI) method, the Mixture-Model approach is improved to combine all kinematic data, including the coordinative position( $\vec{x}$ ), proper motion ( $\vec{\mu}$ ) and radial velocity( v ), to separate the motion of the cluster from field stars, as well as to determine the intrinsic kinematic status and dynamical effects of the cluster, such as the mass segregation, anisotropy etc.. Meanwhile, the membership probability of individual stars are estimated as by product results. This method has been testified by simulation of toy models and also successfully used for well studied open clusters, such as M67 and NGC188. It is expected to largely help the studies of open clusters while combine the coming GAIA data.