Deriving structural parameters of semi-resolved star clusters. FitClust: a program for crowded fields

TL;DR

This paper introduces FitClust, an automated program that accurately derives structural parameters of semi-resolved star clusters in crowded fields by modeling brightness, stars, and background, improving measurement precision.

Contribution

The paper presents a novel automated tool, FitClust, capable of deriving structural parameters of semi-resolved star clusters while accounting for individual stars and variable sky background.

Findings

FitClust effectively models semi-resolved clusters in crowded fields.

Accounting for stars and background improves parameter accuracy.

Uncertainty remains dominated by unresolved star noise.

Abstract

Context. An automatic tool to derive structural parameters of semi-resolved star clusters located in crowded stellar fields in nearby galaxies is needed for homogeneous processing of archival frames. Aims. We have developed a program that automatically derives the structural parameters of star clusters and estimates errors by accounting for individual stars and variable sky background. Methods. Models of observed frames consist of the cluster's surface brightness distribution, convolved with a point spread function; the stars, represented by the same point spread function; and a smoothly variable sky background. The cluster's model is fitted within a large radius by using the Levenberg-Marquardt and Markov chain Monte Carlo algorithms to derive structural parameters, the flux of the cluster, and individual fluxes of all well-resolved stars. Results. FitClust, a program to derive structural parameters of semi-resolved clusters in crowded stellar fields, was developed and is available for free use. The program was tested on simulated cluster frames, and was used to measure clusters of the M31 galaxy in Subaru Suprime-Cam frames. Conclusions. Accounting for bright resolved stars and variable sky background significantly improves the accuracy of derived structural parameters of star clusters. However, their uncertainty remains dominated by the stochastic noise of unresolved stars.