Scaling up the detail in particle collisions Factorization and resummation for predictions of multi-differential cross sections

Open Access
Authors
Supervisors
Cosupervisors
Award date 29-11-2019
ISBN
  • 9789463239141
Number of pages 291
Organisations
  • Faculty of Science (FNWI) - Institute of Physics (IoP) - Institute for Theoretical Physics Amsterdam (ITFA)
Abstract
The particles that form the most fundamental building blocks of nature, as well as the interactions between them, are described by the Standard Model. This theory is tested through high-energy scattering experiments performed at particle colliders such as the Large Hadron Collider. As the accuracy of measurements at these colliders increases, ever more exclusive final states can be considered. To match the demand of current experiments, precise theoretical predictions of increasingly differential cross sections are required.
When multiple observables that constrain radiation are measured on a particular final state, logarithms of the ratios of their corresponding scales occur in the cross section. When these scales are widely separated, the logarithms grow large and require resummation.
The research described in this thesis is aimed at both improving the resummation accuracy of some existing frameworks, as well as developing new methods for the simultaneous resummation of multiple types of logarithms. The considered measurements are sensitive to infrared (soft and collinear) radiation that arises in the context of Quantum Chromodynamics. By using the Soft-Collinear Effective Theory, factorization formulas that separate the various energy scales may be obtained. Through the renormalization group evolution of the ingredients in such a factorization, the large logarithms of the ratios of said scales may be resummed and reliable predictions can be obtained. These fine-grained predictions are vital in the current search for the faintest hints of new physics from beyond the Standard Model that might be hidden within the experimental data.
Document type PhD thesis
Language English
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