Regularised unfolding with a discrete-valued penalty function

TL;DR

This paper introduces a novel regularisation method for discrete unfolding problems using a discrete-valued penalty function, which simplifies parameter tuning and yields satisfactory results in toy studies.

Contribution

It proposes a new regularisation scheme based on a discrete-valued penalty function that does not require parameter adjustment, improving discrete unfolding.

Findings

Satisfactory results in toy studies

No need for regularisation parameter tuning

Comparison with cutoff-regularisation shows effectiveness

Abstract

Regularisation allows one to handle ill-posed inverse problems. Here we focus on discrete unfolding problems. The properties of the results are characterised by the consistency between measurements and unfolding result and by the posterior response matrix. We introduce a novel regularisation scheme based on a discrete-valued penalty function and compare its performance to that of a simple cutoff-regularisation. The discrete-valued penalty function does not require a regularisation parameter that needs to be adjusted on a case-by-case basis. In toy studies very satisfactory results are obtained.