English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We consider the high energy physics unfolding problem where the goal is toestimate the spectrum of elementary particles given observations distorted bythe limited resolution of a particle detector. This important statisticalinverse problem arising in data analysis at the Large Hadron Collider at CERNconsists in estimating the intensity function of an indirectly observed Poissonpoint process. Unfolding typically proceeds in two steps: one first produces aregularized point estimate of the unknown intensity and then uses thevariability of this estimator to form frequentist confidence intervals thatquantify the uncertainty of the solution. In this paper, we propose forming thepoint estimate using empirical Bayes estimation which enables a data-drivenchoice of the regularization strength through marginal maximum likelihoodestimation. Observing that neither Bayesian credible intervals nor standardbootstrap confidence intervals succeed in achieving good frequentist coveragein this problem due to the inherent bias of the regularized point estimate, weintroduce an iteratively bias-corrected bootstrap technique for constructingimproved confidence intervals. We show using simulations that this enables usto achieve nearly nominal frequentist coverage with only a modest increase ininterval length. The proposed methodology is applied to unfolding the $Z$ bosoninvariant mass spectrum as measured in the CMS experiment at the Large HadronCollider.

Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification