Thesis Topic: Dynamical models of multiple globular cluster populations around massive galaxies

hilker

 

Thesis Supervisors: Michael Hilker and Adriano Agnello 

 

 

 

 

Abstract:


Massive galaxies reside in massive Dark Matter (DM) halos. Constraining the density profile of the dark halos, as well as the orbits of visible matter within them, is key to reconstructing the assembly history of massive galaxies and its interplay with their host halos.

For nearby massive galaxies, we can exploit halo populations of Globular Clusters (GCs) extending out to ~100 kpc, probing regions beyond the reach of starlight observations. The GC systems of massive galaxies have typically multiple populations with different chemistry and radial distributions, probing different regions of the same halo. The study of GC chemo-dynamics has received considerable impetus since the advent of spectroscopic datasets, counting hundreds or thousands of CGs, yielding for each host galaxy a wealth of kinematic information over diverse lengthscales.


A robust separation of multiple tracer populations enables the study of dark matter halos as well as reconstructing the orbits of GCs, whence information on the mass accretion history. Different techniques have been devised to separate multiple GC populations and perform dynamical analyses. Despite the success of some of these in reproducing the results of independent observations, such as extended starlight profiles from very deep observations, none of them has been functionally tested by itself. This may introduce considerable bias when inferring DM profiles and GC orbits,  especially for large datasets where the uncertainties are limited by systematics, an effect that has not been quantified yet.

Within this project, the PhD candidate will examine mock models of multiple GC populations around massive galaxies, devising `blinded' functional tests and comparing the possible sources of bias in different techniques. This is expected to set the standard for future analyses of large kinematic datasets and can be considered as the preparation of a wider PhD project on the evolution of massive galaxies across cosmic time.