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Cosmology with dark matter maps

Jeffrey, Niall; (2019) Cosmology with dark matter maps. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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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.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Cosmology with dark matter maps
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2019. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10086027
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