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Title: Statistical methods in weak gravitational lensing
Author: Kacprzak, T.
ISNI:       0000 0004 5365 0234
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2015
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This thesis studies several topics in the area of weak gravitational lensing and addresses some key statistical problems within this subject. A large part of the thesis concerns the measurement of galaxy shapes for weak gravitational lensing and the systematics they introduce. I focused on studying two key effects, typical for model-fitting shape measurement methods. First is noise bias, which arises due to pixel noise on astronomical images. I measure noise bias as a function of key galaxy and image parameters and found that the results are in good agreement with theoretical predictions. I found that if the statistical power of a survey is to be fully utilised, noise bias effects have to be calibrated. The second effect is called model bias, which stems from using simple models to fit galaxy images, which can have more complicated morphologies. I also investigate the interaction of these two systematics. I found model bias to be small for ground-based surveys, rarely exceeding 1%. Its interaction with noise bias was found to be negligible. These results suggest that for ongoing weak lensing surveys, noise bias is the dominant effect. Chapter 5 describes my search for a weak lensing signal from dark matter filaments in CFHTLenS fields. It presents a novel, model-fitting approach to modelling the mass dis- tribution and combining measurements from multiple filaments. We find that CFHTLenS data does provide very good evidence for dark matter filaments, with detection significance of 3.9σ for the filament density parameter relative to mean halo density of connected halos at their R200. For 19 pairs of the most massive halos, the integrated density contrast of filaments was found on a level of 1 · 1013M⊙/h. The appendices present my contribution to three other papers. They describe practical applications of the calibration of noise bias in the GREAT08 challenge and the Dark Energy Survey. I also present the results of the validation of reconvolution and image rendering using FFTs in the GalSim toolkit.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available