Determination of uncertainties in parton densities

Hunt-Smith, N. T.; Accardi, A.; Melnitchouk, W.; Sato, N.; Thomas, A. W.; White, M. J.

doi:10.1103/PhysRevD.106.036003

Title: Determination of uncertainties in parton densities

Journal Article · Tue Aug 02 00:00:00 EDT 2022 · Physical Review D

DOI:https://doi.org/10.1103/PhysRevD.106.036003· OSTI ID:1879602

;

; Sato, N.;

; White, M. J.

We review various methods used to estimate uncertainties in quantum correlation functions, such as parton distribution functions (PDFs). Using a toy model of a PDF, we compare the uncertainty estimates yielded by the traditional Hessian and data resampling methods, as well as from explicitly Bayesian analyses using nested sampling or hybrid Markov chain Monte Carlo techniques. We investigate how uncertainty bands derived from neural network approaches depend on details of the network training, and how they compare to the uncertainties obtained from more traditional methods with a specific underlying parametrization. Our results show that utilizing a neural network on a simplified example of PDF data has the potential to inflate uncertainties, in part due to the cross-validation procedure that is generally used to avoid overfitting data.

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Research Organization:: Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Nuclear Physics (NP); Australian Research Council

Grant/Contract Number:: AC05-06OR23177; SC0008791; CE200100008; DP180102209; DP180100497

OSTI ID:: 1879602

Alternate ID(s):: OSTI ID: 1880020

Report Number(s):: JLAB-THY-22-3638; DOE/OR/23177-5514; arXiv:2206.10782; PRVDAQ; 036003

Journal Information:: Physical Review D, Journal Name: Physical Review D Vol. 106 Journal Issue: 3; ISSN 2470-0010

Publisher:: American Physical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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