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Bayesian Inference of Kinematics and Mass Segregation of Open Cluster

Published online by Cambridge University Press:  31 March 2017

Z. Shao
Affiliation:
Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China email: [email protected]
X. Xie
Affiliation:
Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China email: [email protected]
L. Chen
Affiliation:
Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China email: [email protected]
J. Zhong
Affiliation:
Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China email: [email protected]
J. Hou
Affiliation:
Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China email: [email protected]
C-C. Lin
Affiliation:
Shanghai Astronomical Observatory, CAS, 80 Nandan Road, Shanghai 200030, China email: [email protected]
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Abstract

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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.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2017 

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