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Title: Tipping scales in galaxy surveys : star/galaxy separation and scale-dependent bias
Author: Soumagnac, M. T.
ISNI:       0000 0004 5364 4678
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2015
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In the first part of this thesis, we address the problem of separating stars from galaxies in future large photometric surveys. We derive the science requirements on star/galaxy separation, for measurement of the cosmological parameters with the Gravitational Weak Lensing and Large Scale Structure probes, in chapter 2. We formulate the requirements in terms of the completeness and purity provided by a given star/galaxy classifier. In order to achieve these requirements, we propose a new method for star/galaxy separation in chapter 3, combining Principal Component Analysis with an Artificial Neural Network. When tested on simulations of the Dark Energy Survey (DES), this multi-parameter approach improves upon purely morphometric classifiers (such as the classifier implemented in SExtractor), especially at faint magnitudes. Chapter 4 is dedicated to the testing of this tool on real data, namely the recent internal release of DES Science Verification data. In the second part and last chapter of this thesis, chapter 5, we develop a method to detect the modulation by Baryonic Acoustic Oscillations of the density ratio of baryon to dark matter across large regions of the Universe. Such a detection would provide a direct measurement of a difference in the large-scale clustering of mass and light and a confirmation of the standard cosmological paradigm from a different angle than any other measurement. We measure the number density correlation function and the luminosity weighted correlation function of the DR10 releases of the Baryon Oscillation Spectroscopic Survey (BOSS), and fit a model of scale dependent bias to our measurement. Although our measurement is compatible with previous theoretical predictions, more accurate data is needed to prove or disprove this effect.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available