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相关论文: PDF reweighting in the Hessian matrix approach

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We discuss the Hessian PDF reweighting - a technique intended to estimate the effects that new measurements have on a set of PDFs. The method stems straightforwardly from considering new data in a usual $\chi^2$-fit and it naturally…

高能物理 - 唯象学 · 物理学 2014-08-21 Hannu Paukkunen , Pia Zurita

Hessian PDF reweighting, or "profiling", has become a widely used way to study the impact of a new data set on parton distribution functions (PDFs) with Hessian error sets. The available implementations of this method have resorted to a…

高能物理 - 唯象学 · 物理学 2019-09-27 Kari J. Eskola , Petja Paakkinen , Hannu Paukkunen

We develop in more detail our reweighting method for incorporating new datasets in parton fits based on a Monte Carlo representation of PDFs. After revisiting the derivation of the reweighting formula, we show how to construct an unweighted…

New hard-scattering measurements from the LHC proton-lead run have the potential to provide important constraints on the nuclear parton distributions and thus contributing to a better understanding of the initial state in heavy ion…

高能物理 - 唯象学 · 物理学 2015-06-17 Nestor Armesto , Juan Rojo , Carlos A. Salgado , Pia Zurita

We discuss a Bayesian methodology for the solution of the inverse problem underlying the determination of parton distribution functions (PDFs). In our approach, Gaussian Processes (GPs) are used to model the PDF prior, while Bayes theorem…

高能物理 - 唯象学 · 物理学 2024-07-03 Alessandro Candido , Luigi Del Debbio , Tommaso Giani , Giacomo Petrillo

A robust uncertainty estimate in global analyses of Parton Distribution Functions (PDFs) is essential at the Large Hadron Collider (LHC), especially in view of the high-precision data anticipated by experimentalists in the High-Luminosity…

高能物理 - 唯象学 · 物理学 2026-04-14 Mark N. Costantini , Luca Mantani , James M. Moore , Maria Ubiali

We discuss how to apply the Hessian method (i) to predict the impact of a new data set (or sets) on the parton distribution functions (PDFs) and their errors, by producing an updated best-fit PDF and error PDF sets, such as the CTEQ-TEA…

高能物理 - 唯象学 · 物理学 2018-11-14 Carl Schmidt , Jon Pumplin , C. -P. Yuan

We develop a methodology for the construction of a Hessian representation of Monte Carlo sets of parton distributions, based on the use of a subset of the Monte Carlo PDF replicas as an unbiased linear basis, and of a genetic algorithm for…

高能物理 - 唯象学 · 物理学 2015-09-02 Stefano Carrazza , Stefano Forte , Zahari Kassabov , Jose Ignacio Latorre , Juan Rojo

Parton distribution functions (PDFs) form an essential part of particle physics calculations. Currently, the most precise predictions for these non-perturbative functions are generated through fits to global data. A problem that several PDF…

高能物理 - 唯象学 · 物理学 2025-09-04 Mengshi Yan , Tie-Jiun Hou , Zhao Li , Kirtimaan Mohan , C. -P. Yuan

Determinations of the proton's collinear parton distribution functions (PDFs) are emerging with growing precision due to increased experimental activity at facilities like the Large Hadron Collider. While this copious information is…

高能物理 - 唯象学 · 物理学 2019-01-24 Bo-Ting Wang , T. J. Hobbs , Sean Doyle , Jun Gao , Tie-Jiun Hou , Pavel M. Nadolsky , Fredrick I. Olness

The Hessian method is widely applied in the global analysis of parton distribution functions (PDFs), which uses a set of orthogonal eigenvectors to give predictions of a physical observable. Its uncertainty is estimated based on the…

高能物理 - 唯象学 · 物理学 2025-09-12 Wenxiao Zhan , Siqi Yang , Minghui Liu , Liang Han , Daniel Stump , C. -P. Yuan

We present a method developed by the NNPDF Collaboration that allows the inclusion of new experimental data into an existing set of parton distribution functions without the need for a complete refit. A Monte Carlo ensemble of PDFs may be…

高能物理 - 唯象学 · 物理学 2015-06-03 Francesco Cerutti , Nathan Hartland

We present a method for incorporating the information contained in new datasets into an existing set of parton distribution functions without the need for refitting. The method involves reweighting the ensemble of parton densities through…

A determination of Parton Distribution Functions (PDFs) from a global fit to a dataset including measurements from the LHC has been performed. The determinations have been carried out according to the NNPDF methodology, leading to a fit…

高能物理 - 唯象学 · 物理学 2014-11-04 Nathan Hartland

We explore connections between two common methods for quantifying the uncertainty in parton distribution functions (PDFs), based on the Hessian error matrix and Monte-Carlo sampling. CT14 parton distributions in the Hessian representation…

In an earlier publication, we introduced the software package, {\tt \texttt{ePump}} (error PDF Updating Method Package), that can be used to update or optimize a set of parton distribution functions (PDFs), including the best-fit PDF set…

高能物理 - 唯象学 · 物理学 2019-12-18 Tie-Jiun Hou , Zhite Yu , Sayipjamal Dulat , Carl Schmidt , C. -P. Yuan

In the context of a recent CTEQ6.6 global analysis, we review a new technique for studying correlated theoretical uncertainties in hadronic observables associated with imperfect knowledge of parton distribution functions (PDFs). The…

高能物理 - 唯象学 · 物理学 2008-09-08 Pavel M. Nadolsky

We investigate the Monte Carlo approach to propagation of experimental uncertainties within the context of the established "MSTW 2008" global analysis of parton distribution functions (PDFs) of the proton at next-to-leading order in the…

高能物理 - 唯象学 · 物理学 2012-08-13 G. Watt , R. S. Thorne

The choice of data that enters a global QCD analysis can have a substantial impact on the resulting parton distributions and their predictions for collider observables. One of the main reasons for this has to do with the possible presence…

高能物理 - 唯象学 · 物理学 2014-09-11 Juan Rojo

We discuss the Bayesian approach to the solution of inverse problems and apply the formalism to analyse the closure tests performed by the NNPDF collaboration. Starting from a comparison with the approach that is currently used for the…

高能物理 - 唯象学 · 物理学 2022-05-04 Luigi Del Debbio , Tommaso Giani , Michael Wilson
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