An Inverse-Problem Approach to Cluster Dynamics

We propose a new nonparametric algorithm that can be implemented to study and characterize stellar clusters. The scheme attempts to simultaneously recover the stellar distribution function and the cluster potential by using projected radii and velocity information about the cluster members. The pair of these functions that is most consistent with the input data is detected by the Metropolis algorithm. In this work, the cluster characteristics recovered by the algorithm (``CHASSIS'') are calibrated against N-body realizations of two clusters, the Hyades and the Arches. The cluster mass and line-of-sight projected velocity dispersion profiles are correctly reproduced by the algorithm when the cluster obeys the assumption used in the code, namely, isotropy in phase space. The results recovered by the code are shown to be insensitive to the choice of the initial parameters. The results are also not influenced by increasing the number of input data points as long as this number exceeds a minimum value, which is moderately low for an input data set that obeys the assumptions of isotropy and sphericity.