Densities mixture unfolding for data obtained from detectors with finite resolution and limited acceptance

TL;DR

This paper introduces a Mixture Density Model-based method for correcting experimental data distortions caused by finite detector resolution and limited acceptance, providing a regularized, non-negative, and adaptable solution suitable for multidimensional problems.

Contribution

It presents a novel approach combining mixture density models, adaptive smoothing, and the non-negative garrotte method for unfolding data affected by detector limitations.

Findings

Effective in one and two-dimensional examples

Provides smooth, non-negative solutions

Applicable to multidimensional data

Abstract

A procedure based on a Mixture Density Model for correcting experimental data for distortions due to finite resolution and limited detector acceptance is presented. Addressing the case that the solution is known to be non-negative, in the approach presented here, the true distribution is estimated by a weighted sum of probability density functions with positive weights and with the width of the densities acting as a regularisation parameter responsible for the smoothness of the result. To obtain better smoothing in less populated regions, the width parameter is chosen inversely proportional to the square root of the estimated density. Furthermore, the non-negative garrotte method is used to find the most economic representation of the solution. Cross-validation is employed to determine the optimal values of the resolution and garrotte parameters. The proposed approach is directly applicable to multidimensional problems. Numerical examples in one and two dimensions are presented to illustrate the procedure.