TUnfold: an algorithm for correcting migration effects in high energy physics

TL;DR

TUnfold is a versatile algorithm designed to correct for migration and background effects in multi-dimensional high energy physics data, utilizing regularization techniques and uncertainty propagation.

Contribution

It introduces a Tikhonov regularization-based method with advanced parameter determination and error handling, integrated into the ROOT framework.

Findings

Supports background subtraction and error propagation

Uses L-curve method for regularization parameter selection

Handles multi-dimensional distributions effectively

Abstract

TUnfold is a tool for correcting migration and background effects in high energy physics for multi-dimensional distributions. It is based on a least square fit with Tikhonov regularisation and an optional area constraint. For determining the strength of the regularisation parameter, the L-curve method and scans of global correlation coefficients are implemented. The algorithm supports background subtraction and error propagation of statistical and systematic uncertainties, in particular those originating from limited knowledge of the response matrix. The program is interfaced to the ROOT analysis framework.