AbstractThe properties of galaxies, such as the galaxy red fraction and galaxy stellar mass function, have been shown to depend upon their environment in the local Universe. Large scale photometric surveys such as the DES and in the future Euclid, will be vital to gain insight into the evolution of galaxy properties and the role of environment through cosmic time. Large samples come at the cost of redshift precision and this affects the measurement of galaxy environment.
In this thesis an analysis pipeline is constructed to derive galaxy parameters including absolute magnitudes, stellar masses and galaxy environments. The analysis pipeline consists of well established components, such as HYPERZ, that performs SED fitting and components that I have developed and tested, including codes to compute galaxy environment. Five methods to compute galaxy environment are implemented, including three fixed aperture methods, based on spheres, cylinders and cones, the Nth nearest neighbour method and the adaptive Gaussian method. The codes are optimized and parallelized and are executed on Portsmouth’s high performance computer cluster. The codes are thoroughly tested using mock data. Further testing is conducted employing GAMA data, with an external collaborator. The pipeline is applied to two datasets and the results lead to two scientific papers: Etherington &Thomas (2015) and Etherington et al. (in DES collaboration review).
The first study is based on a low redshift sample drawn from the SDSS. Spectroscopic and photometric redshifts and also simulated photometric redshifts with a range of uncertainties are employed to study the impact of photometric redshift uncertainty on measures of environment as a function of the aperture parameters. The photometric environments are found to have a smaller dynamic range compared to the spectroscopic measurements because uncertain redshifts scatter galaxies from dense environments into less dense environments. With the optimal aperture parameter values, even for large redshift uncertainties, ∼ 0.1, there is a Spearman Rank Correlation Coefficient of ∼ 0.4 between the photometric measurements and the spectroscopic benchmark environments. This is sufficient to extract an environment signal from large scale photometric surveys.
The second study in this thesis is based on the science verification data from the
DES. This is the first set of observations from the survey. This study uses ∼3.2 million galaxies from the SPT-East (South Pole Telescope) field that covers approximately 100 sq. deg. of the sky. From the grizY photometry the analysis pipeline is used to derive galaxy stellar masses and absolute magnitudes. The errors on these properties are assessed using Monte-Carlo simulations sampled from the full photometric redshift probability distributions. Galaxy environments are computed using a fixed conical aperture method, for a range of scales. Galaxy environment probability distribution functions are constructed and the dependence of the environment errors on the aperture parameters is investigated. The environment components of the galaxy stellar mass function for the redshift range: 0.15 < z < 1.05 are calculated. For z < 0.75 it is found that the fraction of massive galaxies is larger in high density environment than low density environments. The low and high density components converge with increasing redshift to z ∼ 1.0 where the shapes of the mass function components are indistinguishable. This redshift is important because it marks the transition between an earlier epoch where the mass distribution of galaxies is independent of environment and a later epoch where the mass distribution does depend on galaxy environment. This study shows the build up of high density structures around massive galaxies, through cosmic time.
The results in this thesis demonstrate that large scale photometric surveys can produce competitive galaxy evolution science, enabling further investigations of the role of galaxy environment. This is hugely encouraging for current and future experiments.
|Date of Award||Feb 2016|
|Supervisor||Daniel Thomas (Supervisor), Claudia Maraston (Supervisor) & Diego Capozzi (Supervisor)|