A Bayesian Approach Accounting for Stochastic Fluctuations in Stellar Cluster Properties

TL;DR

This paper introduces a Bayesian method that explicitly incorporates stochastic fluctuations in star cluster properties, improving the accuracy of age and mass estimates from integrated photometry, especially for small, young, or intermediate clusters.

Contribution

It presents a novel Bayesian framework that accounts for stochastic variations in stellar populations, enhancing the reliability of cluster property estimations from unresolved photometric data.

Findings

Bayesian approach effectively models stochastic effects in cluster properties.

Monte-Carlo simulations demonstrate improved age and mass estimates.

Method addresses biases caused by non-Gaussian color distributions.

Abstract

The integrated spectro-photometric properties of star clusters are subject to large cluster-to-cluster variations. They are distributed in non trivial ways around the average properties predicted by standard population synthesis models. This results from the stochastic mass distribution of the finite (small) number of luminous stars in each cluster, stars which may be either particularly blue or particularly red. The color distributions are broad and usually far from Gaussian, especially for young and intermediate age clusters, as found in interacting galaxies. When photometric measurements of clusters are used to estimate ages and masses in conjunction with standard models, biases are to be expected. We present a Bayesian approach that explicitly accounts for stochasticity when estimating ages and masses of star clusters that cannot be resolved into stars. Based on Monte-Carlo simulations, we are starting to explore the probability distributions of star cluster properties obtained given a set of multi-wavelength photometric data.