nProFit: a tool for dynamical models fitting

TL;DR

nProFit is a versatile Python-based tool that fits surface brightness profiles of extragalactic star clusters to theoretical dynamical models, enabling detailed analysis of their structural and dynamical properties.

Contribution

It introduces a general-purpose fitting code for star cluster profiles based on King, Wilson, and Elson models, including advanced parameters and publicly available implementation.

Findings

Successfully fits theoretical models to observed profiles

Provides key dynamical parameters like mass and energy

Demonstrated usefulness in previous studies

Abstract

The surface brightness profiles (SBPs) of star clusters hold invaluable information on the dynamical state of clusters. The observed SBPs of star clusters, especially that of globular clusters, are in good agreement with the SBPs expected for isothermal spheres containing stars of reduced kinetic energies. However, the SBPs of configurations that satisfy these theoretical criteria cannot be uniquely expressed by analytical formulae, which had hindered the analysis of dynamical state of observed clusters in external galaxies. To counter this shortcoming, it has become a practice to use empirical fitting formulae that best represent the core and halo characteristics of theoretical models. We here present a general purpose code, named nProFit, that allows fitting of the surface brightness profiles of extragalactic star clusters to theoretical star clusters, defined by dynamical models of King (1966) and Wilson (1975). In addition, we also incorporated theoretical models that result in power-law surface brightness profiles represented by Elson et al. 1987. The code returns the basic size parameters such as core radius, half-light radius and tidal radius, as well as dynamically relevant parameters, such as the volume and surface density profiles, velocity dispersion profile, total mass and the binding energy for a user-fixed mass-to-light ratio. The usefulness of the code in the dynamical study of extragalactic clusters has been already illustrated in Cuevas-Otahola et al. 2020. The code, which is python-based at the user end, but makes calls to advanced routines in Pyraf and Fortran, is now available for public use. We provide example scripts and mock clusters in the installation package as guide to users.