Clean Data for Hadron Spectroscopy

以前未发现的强相互作用粒子的识别取决于大量实验项目的测量质量。在将模型拟合到可观测值时，很难处理数据集之间的差异。该项目将制定一个框架，以评价来自若干来源的数据，从而提供一套自洽的数据，减少系统不确定性。这种方法被用于评价核数据集以建立核电反应堆模型等领域，其想法是使数据达到新的严格水平，可以通过拟合理论模型的参数进行分析，这样就不太可能产生虚假的结果。该项目的另一个方面将是与理论和现象学合作者合作，开发一个计算昂贵的计算的统计模拟器，以便更彻底地探索参数空间。

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.