Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.798589
Title: Cosmology with dark matter maps
Author: Jeffrey, Niall
ISNI:       0000 0004 8507 8631
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Abstract:
Physics is experiencing an exciting period of exploration into the nature of dark energy, dark matter, and gravitation. With 95% of the mass-energy of the Universe still unexplained, the answers to many further fundamental questions of astro-, theoretical- and particle-physics are being hampered. In the coming years, DES, HSC, KiDS, Euclid and LSST will image billions of galaxies, aiming to use observational data from the late Universe to infer cosmological parameters and compare cosmological models. One of the most promising observables is the weak gravitational lensing effect. Using the statistical power from many small distortions, called shear, DES has provided excellent constraints. However, the standard 2-point statistics do not capture the full information in the data. In the late Universe, gravitational collapse has led to a highly non-Gaussian density field, for which 2-point correlations are not a unique statistical description, and even all N-point functions cannot completely characterize. The research presented in this thesis focuses on methods to reconstruct mass maps from DES weak lensing data and using map-based statistics to infer cosmological parameters and assess theoretical models in a principled Bayesian framework. In Chapter 2, I compare three mass mapping methods with closed-form priors using DES SV data and simulations. In Chapter 3, I demonstrate how the Wiener filter (one of the above methods) computation can be sped up by an order of magnitude using Dataflow Engines (reconfigurable hardware). In Chapter 4, I present a Bayesian hierarchical model which takes into account added uncertainty introduced when noisy simulations are used to generate theoretical predictions. In Chapter 5, with my publicly available DeepMass code, I demonstrate how mass maps reconstructions can be improved (> 10% mean-square-error compared with previously presented methods) using deep learning techniques trained on simulations. In Chapter 6, I discuss future work and the applicability of likelihood-free inference methods for map-based statistics.
Supervisor: Lahav, O. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.798589  DOI: Not available
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