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

Krumholz, M.R.; Adamo, A.; Fumagalli, M.; Calzetti, D.

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.

Citation

Krumholz, M., Adamo, A., Fumagalli, M., & Calzetti, D. (2019). SLUG IV: A Novel Forward-Modelling Method to Derive the Demographics of Star Clusters. Monthly Notices of the Royal Astronomical Society, 482(3), 3550-3566. https://doi.org/10.1093/mnras/sty2896

• Journal Article Type: Article
• Acceptance Date: Oct 23, 2018
• Online Publication Date: Oct 27, 2018
• Publication Date: Jan 21, 2019
• Deposit Date: Nov 21, 2018
• Publicly Available Date: Nov 21, 2018
• Journal: Monthly Notices of the Royal Astronomical Society
• Print ISSN: 0035-8711
• Electronic ISSN: 1365-2966
• Publisher: Royal Astronomical Society
• Peer Reviewed: Peer Reviewed
• Volume: 482
• Issue: 3
• Pages: 3550-3566
• DOI: https://doi.org/10.1093/mnras/sty2896
• Public URL: https://durham-repository.worktribe.com/output/1313751