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Title: Constraining dark matter with renormalisation and global fits
Author: McKay, James
ISNI:       0000 0004 7427 7950
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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I present precision two-loop corrections of O(MeV) mass splittings in electroweak multiplets. These are relevant for both collider phenomenology and dark matter and can affect particle lifetimes by up to 40%. I then show that a commonly used iterative procedure to compute radiatively-corrected pole masses can lead to very different mass splittings than a non-iterative calculation at the same loop order. I show that this has significant phenomenological impact, leading to the conclusion that the iterative procedure should not be used for computing pole masses in situations where electroweak mass splittings are phenomenologically relevant. I then consider global fits to minimal extensions of the Standard Model. Using the GAMBIT package I present a comprehensive study of the scalar singlet dark matter scenario. I then present a follow up global fit including theoretical constraints from physics at high energy scales, and also apply this to a generalisation of the scalar singlet model. I show that solutions exist which stabilise the electroweak vacuum, remain perturbative up to high scales and satisfy current experimental constraints. However, such solutions are only found in a small region of the parameter space soon to be probed by direct detection experiments. Finally I present a detailed comparison of four statistical sampling algorithms. I subject a nested sampler (using the MultiNest package), a Markov Chain Monte Carlo (using the GreAT package), an ensemble Monte Carlo sampler and a differential evolution sampler to a battery of statistical tests. For this I use a realistic physical likelihood function, based on the scalar singlet model of dark matter. I examine the performance of each sampler as a function of its adjustable settings, and the dimensionality of the sampling problem. I evaluate performance on four metrics: optimality of the best fit found, completeness in exploring the best-fit region, number of likelihood evaluations, and total runtime.
Supervisor: Scott, Pat ; Trotta, Roberto Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral