Clean Data for Hadron Spectroscopy

The identification of previously undiscovered strongly interacting particles is dependent on the quality of measurements from a large range of experimental programmes. Discrepancies between data sets are hard to deal with when fitting models to observables. This project will develop a framework to evaluate data from several sources in order to render a self-consistent data set with reduced systematic uncertainties. Such an approach is employed in areas such as the evaluation of nuclear data sets for modelling nuclear power reactors, and the idea is to bring a new level of rigour to data that can be analysed by fitting the parameters of theoretical models, which should be less likely to induce spurious results. A further aspect of the project will be to work with theoretical and phenomenological collaborators to develop a statistical emulator of computationally expensive calculations, in order to allow a more thorough exploration of parameter space.