Data Unfolding: From Problem Formulation to Result Assessment

TL;DR

This paper reviews methods for assessing the quality of data unfolding in experimental physics, emphasizing internal criteria and factors affecting the accuracy of true probability density function estimation.

Contribution

It introduces a comprehensive discussion on internal quality assessment methods and factors influencing the accuracy of unfolding procedures in complex experimental systems.

Findings

Internal criteria can effectively evaluate unfolding quality without external benchmarks.

Factors like resolution, bias, and efficiency significantly impact unfolding accuracy.

The paper provides guidelines for assessing and improving unfolding results.

Abstract

Experimental data in particle and nuclear physics, particle astrophysics, and radiation protection dosimetry are collected using experimental facilities that consist of a complex system of sensors, electronics, and software. Measured spectra or cross sections are considered as Probability Density Functions (PDFs) that deviate from true PDFs due to resolution, bias, and efficiency effects. Unfolding is viewed as a procedure for estimating an unknown true PDF. Reliable estimates of the true PDF are necessary for testing theoretical models, comparing results from different experiments, and combining results from various research endeavors. Both external and internal quality assessment methods can be applied for this purpose. In some cases, external criteria exist to evaluate deconvolution quality. A typical example is the deconvolution of a blurred image, where the sharpness of the restored image serves as an indicator of quality. However, defining such external criteria can be challenging, particularly when a measurement has not been performed previously. This paper discusses various internal criteria for assessing the quality of the results independently of external information, as well as factors that influence the quality of the unfolded distribution.