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Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and…

计算物理 · 物理学 2020-07-21 Juan M Cruz-Martinez , Stefano Carrazza , Roy Stegeman

We present a new regression model for the determination of parton distribution functions (PDF) using techniques inspired from deep learning projects. In the context of the NNPDF methodology, we implement a new efficient computing framework…

高能物理 - 唯象学 · 物理学 2019-09-04 Stefano Carrazza , Juan Cruz-Martinez

Beyond leading-order, perturbative QCD requires a choice of factorisation scheme to define the parton distribution functions (PDFs) and hard-process cross-section. The modified minimal-subtraction ($\overline{\mathrm{MS}}$) scheme has long…

高能物理 - 唯象学 · 物理学 2025-05-12 Stéphane Delorme , Aleksander Kusina , Andrzej Siódmok , James Whitehead

Modern analysis on parton distribution functions (PDFs) requires calculations of the log-likelihood functions from thousands of experimental data points, and scans of multi-dimensional parameter space with tens of degrees of freedom. In…

高能物理 - 唯象学 · 物理学 2022-08-24 DianYu Liu , ChuanLe Sun , Jun Gao

The interest into parton distribution functions (PDFs) and fragmentation functions (FFs) in current high energy physics research is twofold. On the one hand, they are fundamental objects to conduct precision phenomenology studies, e.g. at…

高能物理 - 唯象学 · 物理学 2025-09-22 Tanishq Sharma

The goal of this study is to find a prescription for defining parton distributions (PDFs) which are most appropriate for use in those codes where only LO matrix elements (MEs) are used, as in many Monte Carlo generators. We describe a…

高能物理 - 唯象学 · 物理学 2008-07-15 A. Sherstnev , R. S. Thorne

Neural network algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations which provide an alternative to standard global fitting procedures. We propose a technique based on an interactive neural…

高能物理 - 唯象学 · 物理学 2009-04-30 J. Carnahan , H. Honkanen , S. Liuti , Y. Loitiere , P. R. Reynolds

We discuss the determination of the parton substructure of hadrons by casting it as a peculiar form of pattern recognition problem in which the pattern is a probability distribution, and we present the way this problem has been tackled and…

高能物理 - 唯象学 · 物理学 2020-08-31 Stefano Forte , Stefano Carrazza

One of the most fascinating challenges in the context of parton density function (PDF) is the determination of the best combined PDF uncertainty from individual PDF sets. Since 2014 multiple methodologies have been developed to achieve this…

高能物理 - 唯象学 · 物理学 2016-05-18 Stefano Carrazza , José I. Latorre

Since the first determination of a structure function many decades ago, all methodologies used to determine structure functions or parton distribution functions (PDFs) have employed a common prefactor as part of the parametrization. The…

高能物理 - 唯象学 · 物理学 2022-03-09 Stefano Carrazza , Juan M. Cruz-Martinez , Roy Stegeman

In this proceedings we describe the computational challenges associated to the determination of parton distribution functions (PDFs). We compare the performance of the convolution of the parton distributions with matrix elements using…

高能物理 - 唯象学 · 物理学 2019-09-25 Stefano Carrazza , Juan Cruz-Martinez , Jesús Urtasun-Elizari , Emilio Villa

Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons. As the LHC enters an era of high-precision measurements, a robust PDF determination…

高能物理 - 唯象学 · 物理学 2026-01-21 Amedeo Chiefa , Luigi Del Debbio , Richard Kenway

We discuss a test of the generalization power of the methodology used in the determination of parton distribution functions (PDFs). The "future test" checks whether the uncertainty on PDFs, in regions in which they are not constrained by…

高能物理 - 唯象学 · 物理学 2021-04-21 Juan Cruz-Martinez , Stefano Forte , Emanuele R. Nocera

We present an alternative algorithm to global fitting procedures to construct Parton Distribution Functions (PDFs) parametrizations. The proposed algorithm uses Self-Organizing Maps (SOMs) which at variance with the standard Neural…

高能物理 - 唯象学 · 物理学 2017-08-23 H. Honkanen , S. Liuti , Y. C. Loitiere , D. Brogan , P. Reynolds

We present the main results of our recent papers, where we derived an analytical solution of the QCD evolution equations for parton distribution functions. The valence and non-singlet quark components satisfy the Gross-Llewellyn-Smith and…

高能物理 - 唯象学 · 物理学 2025-10-24 A. V. Kotikov , A. V. Lipatov

A new and simple statistical approach is performed to calculate the parton distribution functions (PDFs) of the nucleon in terms of light-front kinematic variables. Analytic expressions of x-dependent PDFs are obtained in the whole x…

高能物理 - 唯象学 · 物理学 2011-04-07 Lijing Shao , Yunhua Zhang , Bo-Qiang Ma

A new and simple statistical approach is performed to calculate the parton distribution functions (PDFs) of the nucleon in terms of light-front kinematic variables. We do not put in any extra arbitrary parameter or corrected term by hand,…

高能物理 - 唯象学 · 物理学 2009-03-12 Yunhua Zhang , Lijing Shao , Bo-Qiang Ma

In this contribution we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account…

高能物理 - 唯象学 · 物理学 2018-11-15 Rabah Abdul Khalek , Jacob J. Ethier , Juan Rojo

Parton distribution function (PDF) at small $x$ in a fast-moving proton is investigated within an upgraded parton model that includes parton splitting with branching cascades and parton fusion. In the region of moderately small $x$, we…

高能物理 - 唯象学 · 物理学 2026-02-27 M. L. Nekrasov

We present a determination of a set of polarized parton distributions (PDFs) of the nucleon, at next-to-leading order, from a global set of longitudinally polarized deep-inelastic scattering data: NNPDFpol1.0. The determination is based on…

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