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An analytic method to compute star cluster luminosity statistics

da Silva, R.L.; Krumholz, M.R.; Fumagalli, M.; Fall, S.M.

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Authors

R.L. da Silva

M.R. Krumholz

S.M. Fall



Abstract

The luminosity distribution of the brightest star clusters in a population of galaxies encodes critical pieces of information about how clusters form, evolve and disperse, and whether and how these processes depend on the large-scale galactic environment. However, extracting constraints on models from these data is challenging, in part because comparisons between theory and observation have traditionally required computationally intensive Monte Carlo methods to generate mock data that can be compared to observations. We introduce a new method that circumvents this limitation by allowing analytic computation of cluster order statistics, i.e. the luminosity distribution of the Nth most luminous cluster in a population. Our method is flexible and requires few assumptions, allowing for parametrized variations in the initial cluster mass function and its upper and lower cutoffs, variations in the cluster age distribution, stellar evolution and dust extinction, as well as observational uncertainties in both the properties of star clusters and their underlying host galaxies. The method is fast enough to make it feasible for the first time to use Markov chain Monte Carlo methods to search parameter space to find best-fitting values for the parameters describing cluster formation and disruption, and to obtain rigorous confidence intervals on the inferred values. We implement our method in a software package called the Cluster Luminosity Order-Statistic Code, which we have made publicly available.

Citation

da Silva, R., Krumholz, M., Fumagalli, M., & Fall, S. (2014). An analytic method to compute star cluster luminosity statistics. Monthly Notices of the Royal Astronomical Society, 438(3), 2355-2370. https://doi.org/10.1093/mnras/stt2351

Journal Article Type Article
Publication Date Mar 1, 2014
Deposit Date Apr 6, 2014
Publicly Available Date Mar 29, 2024
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 438
Issue 3
Pages 2355-2370
DOI https://doi.org/10.1093/mnras/stt2351
Keywords Methods: data analysis, Methods: numerical, Methods: statistical, Techniques: photometric, Galaxies: star clusters: general

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Copyright Statement
This article has been accepted for publication in Monthly notices of the Royal Astronomical Society © 2014 The Authors Published by Oxford University Press on behalf of Royal Astronomical Society. All rights reserved.





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