Probing the large-scale homogeneity of the universe with galaxy redshift surveys

Modern cosmological observations clearly reveal that the universe contains a hierarchy of clustering. However, recent surveys show a transition to homogeneity on large scales. The exact scale at which this transition occurs is still a topic of much debate. There has been much work done in trying to characterise the galaxy distribution using multifractals. However, for a number of years the size, depth and accuracy of galaxy surveys was regarded as insufficient to give a definitive answer. One of the main problems which arises in a multifractal analysis is how to deal with observational selection effects: i.e. 'masks' in the survey region and a geometric boundary to the survey itself. In this thesis I will introduce a volume boundary correction which is rather similar to the approach developed by Pan and Coles in 2001, but which improves on their angular boundary correction in two important respects: firstly, our volume correction 'throws away' fewer galaxies close the boundary of a given data set and secondly it is computationally more efficient. After application of our volume correction, I will then show how the underlying generalised dimensions of a given point set can be computed. I will apply this procedure to calculate the generalised fractal dimensions of both simulated fractal point sets and mock galaxy surveys which mimic the properties of the recent IRAS PSCz catalogue

Weak lensing predictions for coupled dark energy cosmologies at non-linear scales

We present non-linear weak lensing predictions for coupled dark energy models using the CoDECS simulations. We calculate the shear correlation function and error covariance expected for these models, for forthcoming ground-based (such as DES) and space-based (Euclid) weak lensing surveys. We obtain predictions for the discriminatory power of a ground-based survey similar to DES and a space-based survey such as Euclid in distinguishing between $\Lambda$CDM and coupled dark energy models; we show that using the non-linear lensing signal we could discriminate between $\Lambda$CDM and exponential constant coupling models with $\beta_0\geq0.1$ at $4\sigma$ confidence level with a DES-like survey, and $\beta_0\geq0.05$ at $5\sigma$ confidence level with Euclid. We also demonstrate that estimating the coupled dark energy models' non-linear power spectrum, using the $\Lambda$CDM Halofit fitting formula, results in biases in the shear correlation function that exceed the survey errors.Comment: 9 pages, 5 figures, v2: accepted for publication in MNRA

MulGuisin, a Topological Network Finder and its Performance on Galaxy Clustering

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm find networks most efficiently and it defines galaxy networks in a way that most closely resembles human vision.Comment: 15 pages,12 figure

Graph Database Solution for Higher Order Spatial Statistics in the Era of Big Data

We present an algorithm for the fast computation of the general $N$-point spatial correlation functions of any discrete point set embedded within an Euclidean space of $\mathbb{R}^n$. Utilizing the concepts of kd-trees and graph databases, we describe how to count all possible $N$-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths $<r_{max}$. Through bench-marking we show the computational advantage of our new graph based algorithm over more traditional methods. We show that all 3-point configurations up to and beyond the Baryon Acoustic Oscillation scale ($\sim$200 Mpc in physical units) can be performed on current SDSS data in reasonable time. Finally we present the first measurements of the 4-point correlation function of $\sim$0.5 million SDSS galaxies over the redshift range $0.43<z<0.7$.Comment: 9 pages, 8 figures, submitte