Models, data, and unobservable phenomena in physics

Abstract

This thesis provides a model-based philosophical investigation of the epistemology and methodology of modern physics, based on three main axes: (i) the relationship between models and background theories, (ii) the relationship between models and experimental data, and (iii) the observation of unobservable entities in physics, such as dark matter particles.
The first part of the thesis (Chapters 1 and 2) is more generic in nature. It comprises an analysis and synthesis of the literature on the structure of scientific theories and its chronological development into a literature on the nature and role of models in science. It also provides a chapter-length critical evaluation of the literature on the ontology of models accompanied with a novel Carnapian solution to the metaphysical challenges that appear in the debate.
In the second part of the thesis (Chapters 3, 4, and 5) a more practical approach is adopted, and the three main axes of the thesis are developed respectively. Chapter 3 concerns the relationship of models with background theories in perturbative quantum field theory, in which a new type of models that does not belong to the traditional dichotomy between theoretical and phenomenological models is identified. Chapter 4 concerns the relationship of models with experimental data via the construction of data models. A detailed case study of the experimental tests of the Standard Model at the LHCb experiment is conducted, which leads to a number of interesting conclusions about the nature of data models, and the two distinctions between raw/processed data and real/simulated data. Finally, Chapter 5 provides a novel framework for the epistemology of dark matter observation, via which it is argued that a partial explanation for the slow progress in this field is due to the fact that robustness arguments from the variability of experiments are significantly limited.

Date of Award	30 Jun 2022
Original language	English
Awarding Institution	University of Bristol
Sponsors	SWW Doctoral Training Partnership Panel
Supervisor	Karim Thebault (Supervisor) & Adam Toon (Supervisor)