Data mining to constrain new physics in the top sector

Brown, Stephen Edward (2025) Data mining to constrain new physics in the top sector. PhD thesis, University of Glasgow.

Abstract

We explore the Standard Model Effective Field Theory as a (mostly) modelagnostic route to obtaining data–driven limits on new physics. After briefly coveringsome foundational concepts, we introduce a modular and extensible framework thatenables data acquisition, analysis, and the fitting of EFT operators to experimentaldata from particle colliders. A global fit to top quark data is then performed, findinggood agreement with the Standard Model in most cases, and competitive resultscompared to a contemporaneous study. We then investigate, through the lens oftop partial compositeness and anomalous weak top quark couplings, the expectedlimiting factors for both HL–LHC and FCC–hh. We find the key factors in sensitivityin these scenarios to be theoretical uncertainties. We lastly explore the use of graph neural networks to boost sensitivity to EFT contributions, via what amounts to non–rectangular phase space cuts based on model classification, finding significant improvements to be possible in both individual and profiled constraints.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: Q Science > QB Astronomy; Q Science > QC Physics
• Colleges/Schools: College of Science and Engineering > School of Physics and Astronomy
• Supervisor's Name: Miller, Dr. David J. and Buckley, Professor Andy
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85144
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 02 Jun 2025 09:01
• Last Modified: 02 Jun 2025 09:04
• Thesis DOI: 10.5525/gla.thesis.85144
• URI: https://theses.gla.ac.uk/id/eprint/85144

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