Optimal observables and BSM model reinterpretation

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Searches for new physics at particle colliders have historically been dominated by direct-search data analyses, with event selections driven by the features of few-parameter models. This approach has served relatively well for the first decade of the LHC, but as all such searches have drawn a blank there is a shift afoot to also derive BSM constraints from more generic, model-independent measurements. In this project, we will attempt to tread a middle ground between dedicated searches and generic measurements, by identification and measurement of observables designed to be optimally balanced between a priori constraining power on non-simplifed models, and the correlated structure of experimental uncertainties which can dilute that power. Making best use of Tomek's theoretical physics background and research experience, the project will include both phenomenology (measurement-oriented theory) and experimental components. The starting point for this work will be extending the Contur study of vector-like quarks to extend the number of simultaneous model parameters considered, and to integrate Contur into the Gambit BSM global fit system. By studying and optimising proposed new observables against pseudo-data, a new set with maximal constraining power relative to current limits will be derived. We will then perform a detector-corrected measurement of these observables with the ATLAS detector, and include the results in the Rivet system and hence an updated Gambit/Contur fit. To ensure model-independence of the detector corrections, detailed detector simulation and reconstruction will be needed for many points through the model parameter space. As these steps are very computationally expensive, running many different event samples will not be an option: we will hence explore the use of deep neural network reweighting to efficiently sample the model space at reconstruction level.