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Modelling star clusters with potential escapers

Claydon, Ian (2019) Modelling star clusters with potential escapers Doctoral thesis, University of Surrey.

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Abstract

By investigating the behaviour of potential escapers in evolutionary models of globular clusters, I make a prediction for the kinematics in the outskirts and develop a simple, static model which includes the effect s of potential escapers and can be fit to observational data. I investigate the behaviour of potential escapers in N-body models to determine if a unique signal exists that can discriminate between alternative theories to explain the peculiarities in observations of GCs outskirts. By running a series of N-body simulations of globular clusters, with varying IMF, galactic potential and orbital eccentricity, I make a prediction for the lower limit of the velocity dispersion profile the velocity dispersion anisotropy and the rotation in the cluster if the outskirts dynamics are primarily determined by the existence of PEs. And as these observables depend on the galactic potential, can also provide a means of inferring properties of the host galaxy. Further to this, I begin a method of including the effects of potential escapers (PEs) as a static, distribution function based model, which can be quickly fit to observational data to scan a wide range of parameters. This model, the spherical potential escaper stitched model (SPES), is able to accurately fit the velocity dispersion and density profiles in outskirts of MW GCs where existing models can fail. This is especially pertinent with the wealth of new data of stars in the outskirts of GCs provided by the Gaia mission. Additionally, it is useful to include the effects of the galactic potential in the model for two reasons 1) to be able to fit on the slope of the enclosed mass profile 2) to accurately reproduce the critical energy of the system. Therefore I present a method of including both the effects of PEs and the galactic potential in the aspherical potential escapers stitched model (APES).

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors : Claydon, Ian
Date : November 2019
Funders : European Research Council (ERC)
DOI : 10.15126/thesis.00852968
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/THSGieles, Markm.gieles@surrey.ac.uk
Depositing User : Ian Claydon
Date Deposited : 20 Dec 2019 09:28
Last Modified : 20 Dec 2019 09:30
URI: http://epubs.surrey.ac.uk/id/eprint/852968

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