Powered by Indico
v3.2.3
The binary properties of open clusters place crucial constraints on star formation theory and clusters' dynamical evolution. We develop a comprehensive approach that models the color-magnitude diagram (CMD) of the cluster members as the mixture of single stars and photometric unresolved binaries. This method enables us to infer the binary properties, including the binary fraction fb and binary mass-ratio distribution index γq when a power-law is assumed, with high accuracy and precision, which were unfeasible in conventional methods. We employ a modified Gaussian process to determine the main sequence ridge line and its scatter from the observed CMD as model input. As a first example, we apply the method to the open cluster NGC3532 with the \textit{Gaia} DR2 photometry. For the cluster members within a magnitude range corresponding to FGK dwarfs, we obtain fb=0.267±0.019 and γq=−0.10±0.22 for binaries with mass ratio q>0.2. The fb value is consistent with the previous work on NGC3532 and smaller than that of field stars. The close to zero γq indicates that the mass ratios of binaries follow a nearly uniform distribution. For the first time, we unveil that the stars with smaller mass or in the inner region tend to have lower fb and more positive value of γq due to the lack of low mass-ratio binaries. The clear dependences of binary properties on mass and radius are most likely caused by the internal dynamics.
Paper see https://ui.adsabs.harvard.edu/abs/2020ApJ...901...49L/abstract This paper was featured in the AAS Author Series,https://www.youtube.com/watch?v=y2m3ahIyY94&list=PLFhVT3VzlwKrArGdcNhtSCF4NbDBd6K5_&index=4&t=380s
Lu Li is a Ph.D. student at Shanghai Astronomical Observatory. Her research covers broad topics related to open clusters, including the binary stars, mass function, dynamical evolution, and data mining in sky surveys, such as Gaia. At present, she is trying to build a comprehensive statistical model for open clusters photometry, as a response to the challenges raised by the upcoming data flood.
Video record is available wihtin SJTU: https://vshare.sjtu.edu.cn/play/df17736f6ba19550038bce9b72e27479