Optimal strategies for testing physics beyond the Standard Model

This project is about developing new methods for comparison of Beyond Standard Model physics models to data, with emphasis on optimal combination of multiple datasets, and on designing custom observables for maximum expected model sensitivity in the context of current statistical limits. This will extend the ability of analysis-recasting studies to place constraints on the parameters of new models, propose new experimental measurement strategies, and maximise reuse of the LHC dataset. The student will work with statistical algorithms and machine-learning methods, using simulated collider events and public datasets, and collaborate with recasting teams and the LHC BSM Reinterpretation Forum.