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Parton Distributions Need Representative Sampling by Aurore Courtoy; Joey Huston; Pavel Nadolsky; Keping Xie; Mengshi Yan; C.-P. Yuan is a Physics and Astronomy article available to read on EtoBox.

What is Parton Distributions Need Representative Sampling about?

In global QCD fits of parton distribution functions (PDFs), a large part of the estimated uncertainty on the PDFs originates from the choices of parametric functional forms and fitting methodology. We argue that these types of uncertainties can be underestimated with common PDF ensembles in high-stake measurements at the Large Hadron Collider and Tevatron. A fruitful approach to quantify these uncertainties is to view them as arising from sampling of allowed PDF solutions in a multidimensional parametric space. This approach applies powerful insights gained in recent statistical studies of large-scale population surveys and quasi-Monte Carlo integration methods. In particular, PDF fits may be affected by the big data paradox, which stipulates that more experimental data do not automatically raise the accuracy of PDFs-close attention to the data quality and sampling of possible PDF solutions is as essential. To test if the sampling of the PDF uncertainty of an experimental observable is truly representative of all acceptable solutions, we introduce a technique ("a hopscotch scan") based on a combination of parameter scans and stochastic sampling. With this technique, we examine the

Who reads Parton Distributions Need Representative Sampling?

Author

Aurore Courtoy; Joey Huston; Pavel Nadolsky; Keping Xie; Mengshi Yan; C.-P. Yuan

Publisher

American Physical Society (APS)

Published

2023

Language

EN

Field

Physics and Astronomy (Physical Sciences)