标题： 基于数据驱动的稀疏数据插值方法 显示英文标题

标题： Data-driven Approach for Interpolation of Sparse Data

摘要： 强子共振及其性质的研究受到测量数据集的准确性和一致性限制，这些数据集可能来自许多不同的实验。 我们使用高斯过程（GP）构建插值数据集，包括不确定性量化，以便来自不同来源的数据可以在模型拟合中使用而无需任意权重。 GP在任何动力学点预测可观测量的值和不确定性。 贝叶斯推理用于优化GP模型的超参数。 我们通过与生成的伪数据比较证明GP成功地插值了具有量化不确定性的数据。 我们还表明这种方法可以用来调查来自不同来源的数据的一致性。 GP提供了一种稳健且无模型依赖的方法来插值用于强子共振研究的典型数据集，在稀疏数据集中消除了任意权重的局限性。 显示英文摘要

摘要： Studies of hadron resonances and their properties are limited by the accuracy and consistency of measured datasets, which can originate from many different experiments. We have used Gaussian Processes (GP) to build interpolated datasets, including quantification of uncertainties, so that data from different sources can be used in model fitting without the need for arbitrary weighting. GPs predict values and uncertainties of observables at any kinematic point. Bayesian inference is used to optimise the hyperparameters of the GP model. We demonstrate that the GP successfully interpolates data with quantified uncertainties by comparison with generated pseudodata. We also show that this methodology can be used to investigate the consistency of data from different sources. GPs provide a robust, model-independent method for interpolating typical datasets used in hadron resonance studies, removing the limitations of arbitrary weighting in sparse datasets.