Expanding limits of statistical methods for high energy physics

Abstract

The Standard Model serves as the fundamental framework for describing elementary particles and their interactions. Even though all particles within the Standard Model have been discovered, particle searches remain relevant. The upcoming experiments at the Large Hadron Collider, the accelerator famous for the discovery of the Higgs boson, aim to uncover new particles that may provide explanations for missing components of the Standard Model, such as Dark Matter, Neutrino masses, or Gravitational forces. A key contribution of my doctoral studies is a novel method for precise and computationally efficient calculation of the trials factor, a quantity that plays an essential role in telling whether we actually see a new particle in the data. Notably, this research has yielded the SigCorr framework, a versatile tool for constructing analytical pipelines in particle physics. I also had an opportunity to contribute to a range of significant open-source projects through collaborations facilitated by my membership in the Marie Curie Innovative Training Network “INSIGHTS”. Apart from projects closely related to statistical analysis in particle physics, I am proud to be a contributor to the research conducted by CICERO, Center for International Climate Research in Oslo, by providing a tool for climate data visualization. In summary, this doctoral thesis presents groundbreaking statistical methods and software for high-energy physics. Its contributions could greatly improve precision and efficiency in particle searches and statistical analysis in physics and other fields.