Fitting Isochrones to Open Cluster photometric data: A new global optimization tool

TL;DR

This paper introduces a new global optimization method using the Cross-Entropy algorithm to fit isochrones to open cluster data, accurately determining cluster parameters with reduced subjectivity.

Contribution

The paper presents a novel automated isochrone fitting technique that accounts for binary stars and uncertainties, improving accuracy and objectivity over traditional methods.

Findings

Consistent parameter estimates across multiple data sets

Method effectively accounts for binary fractions and uncertainties

Results align with previous studies, validating the approach

Abstract

We present a new technique to fit color-magnitude diagrams of open clusters based on the Cross-Entropy global optimization algorithm. The method uses theoretical isochrones available in the literature and maximizes a weighted likelihood function based on distances measured in the color-magnitude space. The weights are obtained through a non parametric technique that takes into account the star distance to the observed center of the cluster, observed magnitude uncertainties, the stellar density profile of the cluster among others. The parameters determined simultaneously are distance, reddening, age and metallicity. The method takes binary fraction into account and uses a Monte-Carlo approach to obtain uncertainties on the determined parameters for the cluster by running the fitting algorithm many times with a re-sampled data set through a bootstrapping procedure. We present results for 9 well studied open clusters, based on 15 distinct data sets, and show that the results are consistent with previous studies. The method is shown to be reliable and free of the subjectivity of most previous visual isochrone fitting techniques.