An Unfolding Method for High Energy Physics Experiments

TL;DR

This paper introduces a novel unfolding method for high energy physics data that improves the accuracy of correcting for detector effects by combining maximum likelihood fitting, clustering, and regularization techniques.

Contribution

A new unfolding approach based on maximum likelihood fit, clustering, and data-driven regularization, enhancing the accuracy and stability of results in high energy physics experiments.

Findings

Effective handling of finite Monte Carlo statistics using Barlow's method.

Improved regularization technique with data-driven parameter selection.

Enhanced unfolding accuracy in multi-dimensional data.

Abstract

Finite detector resolution and limited acceptance require to apply unfolding methods in high energy physics experiments. Information on the detector resolution is usually given by a set of Monte Carlo events. Based on the experience with a widely used unfolding program (RUN) a modified method has been developed. The first step of the method is a maximum likelihood fit of the Monte Carlo distributions to the measured distribution in one, two or three dimensions; the finite statistic of the Monte Carlo events is taken into account by the use of Barlows method with a new method of solution. A clustering method is used before to combine bins in sparsely populated areas. In the second step a regularization is applied to the solution, which introduces only a small bias. The regularization parameter is determined from the data after a diagonalization and rotation procedure.