Data Unfolding Methods in High Energy Physics

TL;DR

This paper compares various data unfolding methods used in High Energy Physics, analyzing their advantages, disadvantages, and procedures for regularisation strength selection through toy examples.

Contribution

It provides a comparative analysis of multiple unfolding techniques and evaluates regularisation parameter selection methods in the context of High Energy Physics.

Findings

Matrix inversion and iterative methods have different stability properties.

Regularisation strength significantly affects unfolding results.

L-curve and correlation scans are effective for choosing regularisation parameters.

Abstract

A selection of unfolding methods commonly used in High Energy Physics is compared. The methods discussed here are: bin-by-bin correction factors, matrix inversion, template fit, Tikhonov regularisation and two examples of iterative methods. Two procedures to choose the strength of the regularisation are tested, namely the L-curve scan and a scan of global correlation coefficients. The advantages and disadvantages of the unfolding methods and choices of the regularisation strength are discussed using a toy example.