Cluster radius and sampling radius in the determination of cluster membership probabilities

TL;DR

This paper investigates how the choice of sampling radius affects the accuracy of cluster membership probabilities, finding an optimal radius that balances true member identification and field star contamination.

Contribution

It provides an analysis of the dependence of membership probabilities on sampling radius using simulated and real data, highlighting the importance of selecting an optimal radius.

Findings

Optimal sampling radius is close to the cluster radius for best discrimination.

Increasing sampling radius beyond a threshold increases field star contamination.

Different clusters have specific optimal sampling radii around 13-14 arcmin.

Abstract

We analyze the dependence of the membership probabilities obtained from kinematical variables on the radius of the field of view around open clusters (the sampling radius, Rs). From simulated data, we show that the best discrimination between cluster members and non-members is obtained when the sampling radius is very close to the cluster radius. At higher Rs values more field stars tend to be erroneously assigned as cluster members. From real data of two open clusters (NGC 2323 and NGC 2311) we obtain that the number of identified cluster members always increases with increasing Rs. However, there is a threshold Rs value above which the identified cluster members are severely contaminated by field stars and the effectiveness of membership determination is relatively small. This optimal sampling radius is \sim 14 arcmin for NGC 2323 and \sim 13 arcmin for NGC 2311. We discuss the reasons for such behavior and the relationship between cluster radius and optimal sampling radius. We suggest that, independently of the method used to estimate membership probabilities, several tests using different sampling radius should be performed in order to evaluate the existence of possible biases.