Use this URL to cite or link to this record in EThOS:
Title: Discrimination of supersymmetry breaking models from sparticle spectra
Author: Grellscheid, D.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2003
Availability of Full Text:
Full text unavailable from EThOS.
Please contact the current institution’s library for further details.
It is widely expected that over the next few years some evidence of low-energy super-symmetry (SUSY) will be found at collider experiments such as LHC, the Tevatron or a linear collider. This discovery would constitute a significant step in closing the current conceptual gap between experimental observations and proposed fundamental theories, which all make varying predictions about the mechanisms of SUSY breaking and the spectrum of resulting superparticles (sparticles). The focus of the search will therefore soon shift from the discovery of SUSY to more detailed studies of the proposed models of SUSY and SUSY breaking which would make it possible to select or rule out some models. Rather than analyzing the observable consequences of single points in the parameter space of SUSY breaking in detail, or reconstructing SUSY breaking parameters from low scale observables, I will present a procedure aiming to look at various models of SUSY breaking simultaneously. It does so by scanning over wide ranges of their input parameters to create an experimental footprint of each model in “measurement space”. The distance between these footprints gives a direct indication of the minimal set of measurements that is required to separate the models. This makes it possible to decide a priori whether two high scale models can be distinguished experimentally, and what measurement accuracy is necessary to do so. This procedure will be shown in a model scenario motivated by Type I string models. As soon as more than two dimensions are considered in the measurement space, it becomes impossible to obtain the spacing between the footprints by eye. Automatic techniques to judge the separation become necessary; a discussion of these, including the most promising one based on Genetic Algorithms, constitutes the latter part of this thesis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available