Undersmoothed uncertainty quantification for unfolding

Abstract

The high energy physics unfolding problem is an ill-posed inverse problem arising in data analysis at the Large Hadron Collider (LHC) at CERN. Due to the limited resolution of a particle detector, any measurement made at the LHC is smeared and the goal in unfolding is to make inferences about the true particle spectrum given the smeared observations. The problem is typically solved by first forming a regularized point estimator and then using the variability of this estimator to form frequentist confidence intervals. However, these confidence intervals can seriously underestimate the true uncertainty since they ignore the bias that is used to regularize the problem. We propose a solution based on debiasing the unfolded point estimator and implemented this approach in ROOT, a data analysis framework used at CERN.

Publication
Technical Report