Effective field theories for general extensions of the Standard Model with new particles
Metadata
Show full item recordAuthor
Criado Álamo, Juan CarlosEditorial
Universidad de Granada
Departamento
Universidad de Granada. Programa de Doctorado en Física y Ciencias del EspacioMateria
Física de partículas Física teórica de altas energías Teoría cuántica de campos Partículas elementales
Date
2019Fecha lectura
2019-11-29Referencia bibliográfica
Criado Álamo, Juan Carlos. Effective field theories for general extensions of the Standard Model with new particles. Granada: Universidad de Granada, 2019. [http://hdl.handle.net/10481/58301]
Sponsorship
Tesis Univ. Granada.; El trabajo realizado para esta tesis ha sido financiado por la beca FPU14 del MECD y los proyectos FPA2013-47836-C3-2-P y FPA-2016-78220-C3-1-P del MINECO (Fondos FEDER), el proyecto FQM101 de la Junta de Andalucía.Abstract
The current situation in particle physics involves a large number of both theoretical
proposals and experimental measurements. The relation between the two is often
intricate, as every new physics model comes with its own set of motivations and predictions,
and each measurement that is performed has consequences for many theoretical
models. The purpose of this thesis is to set the basis for a general, organized and
efficient way of dealing with these issues.
At first sight, a naive systematic approach to the problem may be devised: pick a
representative set of models together with a sufficiently extensive set of observables,
and compute every observable for each model. This procedure suffers from several
drawbacks. Firstly, it is not easy to decide which models and observables to include:
if there are too many, the task becomes impossible in practice, but one risks not
being general enough otherwise. Secondly, it is inefficient: similar calculations will be
performed many times. Lastly, it does not scale well: if a new kind of model becomes
interesting, one has to recompute the value of all observables; and if a new experiment
is designed, then one has to go back to every model to compute the observables that
are going to be measured. Roughly speaking, the number of calculations that need
to be performed grows as the product of the number of models and the number of
observables of interest.