Data-driven Approach for Interpolation of Sparse Data

TL;DR

This paper presents a Gaussian Process-based method for interpolating sparse hadron resonance data, enabling uncertainty quantification and consistency checks across datasets without arbitrary weighting.

Contribution

It introduces a novel, model-independent Gaussian Process approach for data interpolation and consistency analysis in hadron resonance research.

Findings

GP accurately interpolates data with uncertainty estimates

Method identifies inconsistencies between different data sources

Provides a robust alternative to traditional data weighting

Abstract

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.