SLUG IV: A Novel Forward-Modelling Method to Derive the Demographics of Star Clusters

TL;DR

This paper introduces SLUG IV, a new forward-modeling approach for accurately deriving star cluster demographics from unresolved photometry, effectively handling data heterogeneity, errors, and uncertainties.

Contribution

The paper presents a novel, comprehensive method that improves demographic inference accuracy by fully utilizing data and accounting for uncertainties, implemented in the SLUG code.

Findings

Method accurately recovers cluster demographics from mock data.

Outperforms traditional fitting methods in robustness and accuracy.

Limitations are set by physical models, not statistical uncertainties.

Abstract

We describe a novel method for determining the demographics of a population of star clusters, for example distributions of cluster mass and age, from unresolved photometry. This method has a number of desirable properties: it fully exploits all the information available in a data set without any binning, correctly accounts for both measurement error and sample incompleteness, naturally handles heterogenous data (for example fields that have been imaged with different sets of filters or to different depths), marginalises over uncertain extinctions, and returns the full posterior distributions of the parameters describing star cluster demographics. We demonstrate the method using mock star cluster catalogs and show that our method is robust and accurate, and that it can recover the demographics of star cluster populations significantly better than traditional fitting methods. For realistic sample sizes, our method is sufficiently powerful that its accuracy is ultimately limited by the accuracy of the underlying physical models for stellar evolution and interstellar dust, rather than by statistical uncertainties. Our method is implemented as part of the Stochastically Lighting Up Galaxies (SLUG) stellar populations code, and is freely available.