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Durham e-Theses
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The clustering of dark matter, haloes & galaxies

Angulo de la Fuente, Raúl Esteban (2008) The clustering of dark matter, haloes & galaxies. Doctoral thesis, Durham University.



In this thesis I study the spatial distribution of galaxies, haloes and dark matter particles using a suite of state-of-art cosmological N-body simulations of the growth structure in the Universe. The subjects investigated are conceptually divided into three areas. One line of research, which is made up of Chapters 2 and 3, is to explore the power and limitations of measurements of the imprint of baryonic acoustic oscillations in the clustering of galaxies. I look at how the appearance of the power spectrum is altered by different effects such as nonlinear evolution or redshift space distortions. In these chapters I also explore the best way to analyse survey data and how well new datasets, from both spectroscopic and photometric surveys, will be able to constrain the dark energy equation of state. In a second strand, I study dark matter haloes and their substructures. In Chapter 4, I look at the dependence of the clustering strength of dark haloes on the concentration of the sample. I was able to go beyond the traditional 2-point statistics to extend previous analyses to higher order statistics thanks to the development of a novel way to extract the higher order bias parameters. In Chapter 5, I then zoomed into smaller scales to study a number of properties of the population of substructures within dark haloes. In particular, I consider the mass distribution of substructures as well as their radial distribution and orientation. I also demonstrate that mergers between substructures do indeed occur, which result from objects that are dynamically or geometrically linked before accretion. In the final line of research, presented in Chapters 6 and 7, I develop ideas about how to add more realism to current theoretical predictions for galaxy clustering, and how it would be possible to use low-resolution dark matter simulations to investigate uncertainties in future observations.

Item Type:Thesis (Doctoral)
Award:Doctor of Philosophy
Thesis Date:2008
Copyright:Copyright of this thesis is held by the author
Deposited On:08 Sep 2011 18:33

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