Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification
Mikael Kuusela, Victor M. Panaretos

TL;DR
This paper introduces an empirical Bayes approach combined with bias correction techniques to improve the estimation and uncertainty quantification in the high energy physics unfolding problem, specifically for particle spectra.
Contribution
It proposes a novel empirical Bayes method for regularized intensity estimation and a bias-corrected bootstrap for better confidence intervals in particle spectrum unfolding.
Findings
Achieves nearly nominal frequentist coverage in simulations.
Provides data-driven regularization parameter selection.
Successfully applied to CMS Z boson mass spectrum data.
Abstract
We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a particle detector. This important statistical inverse problem arising in data analysis at the Large Hadron Collider at CERN consists in estimating the intensity function of an indirectly observed Poisson point process. Unfolding typically proceeds in two steps: one first produces a regularized point estimate of the unknown intensity and then uses the variability of this estimator to form frequentist confidence intervals that quantify the uncertainty of the solution. In this paper, we propose forming the point estimate using empirical Bayes estimation which enables a data-driven choice of the regularization strength through marginal maximum likelihood estimation. Observing that neither Bayesian credible…
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