Star Cluster Properties in Two LEGUS Galaxies Computed with Stochastic Stellar Population Synthesis Models

TL;DR

This paper introduces a Bayesian method using stochastic stellar population models to accurately derive star cluster properties from photometry, accounting for sampling effects and providing full probability distributions, applied to LEGUS galaxies.

Contribution

The paper presents a novel Bayesian analysis technique with stochastic models that improves cluster property estimation and robustness over traditional deterministic methods.

Findings

Slug's results are insensitive to most prior choices for individual clusters.

Cluster population properties are robust against analysis assumptions.

Stochastic models fit observational data better than deterministic ones.

Abstract

We investigate a novel Bayesian analysis method, based on the Stochastically Lighting Up Galaxies (slug) code, to derive the masses, ages, and extinctions of star clusters from integrated light photometry. Unlike many analysis methods, slug correctly accounts for incomplete IMF sampling, and returns full posterior probability distributions rather than simply probability maxima. We apply our technique to 621 visually-confirmed clusters in two nearby galaxies, NGC 628 and NGC 7793, that are part of the Legacy Extragalactic UV Survey (LEGUS). LEGUS provides Hubble Space Telescope photometry in the NUV, U, B, V, and I bands. We analyze the sensitivity of the derived cluster properties to choices of prior probability distribution, evolutionary tracks, IMF, metallicity, treatment of nebular emission, and extinction curve. We find that slug's results for individual clusters are insensitive to most of these choices, but that the posterior probability distributions we derive are often quite broad, and sometimes multi-peaked and quite sensitive to the choice of priors. In contrast, the properties of the cluster population as a whole are relatively robust against all of these choices. We also compare our results from slug to those derived with a conventional non-stochastic fitting code, Yggdrasil. We show that slug's stochastic models are generally a better fit to the observations than the deterministic ones used by Yggdrasil. However, the overall properties of the cluster populations recovered by both codes are qualitatively similar.