Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

An analytic method to compute star cluster luminosity statistics.

da Silva, R.L. and Krumholz, M.R. and Fumagalli, M. and Fall, S.M. (2014) 'An analytic method to compute star cluster luminosity statistics.', Monthly notices of the Royal Astronomical Society., 438 (3). pp. 2355-2370.

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.

Item Type:Article
Keywords:Methods: data analysis, Methods: numerical, Methods: statistical, Techniques: photometric, Galaxies: star clusters: general
Full text:(VoR) Version of Record
Download PDF
(1562Kb)
Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1093/mnras/stt2351
Publisher 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.
Date accepted:No date available
Date deposited:19 June 2014
Date of first online publication:March 2014
Date first made open access:No date available

Save or Share this output

Export:
Export
Look up in GoogleScholar