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相关论文: PineAPPL: NLO EW corrections for PDF processes

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We present new sets of nuclear parton distribution functions (nPDFs) at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO). Our analyses are based on deeply inelastic scattering data with charged-lepton and neutrino beams…

高能物理 - 唯象学 · 物理学 2019-12-17 Marina Walt , Ilkka Helenius , Werner Vogelsang

In lattice-QCD calculations of parton distribution functions (PDFs) via large-momentum effective theory, the leading power (twist-three) correction appears as ${\cal O}(\Lambda_{\rm QCD}/P^z)$ due to the linear-divergent self-energy of…

高能物理 - 格点 · 物理学 2023-08-09 Rui Zhang , Jack Holligan , Xiangdong Ji , Yushan Su

The current analysis aims to present the results of a QCD analysis of diffractive parton distribution functions (diffractive PDFs) at next-to-leading order (NLO) accuracy in perturbative QCD. In this new determination of diffractive PDFs,…

高能物理 - 唯象学 · 物理学 2019-09-20 Atefeh Maktoubian , Hossein Mehraban , Hamzeh Khanpour , Muhammad Goharipour

We show for the first time preliminary results of nuclear parton distribution function analysis of charged lepton DIS and Drell-Yan data within the CTEQ framework including error PDFs. We compare our error estimates to estimates of…

高能物理 - 唯象学 · 物理学 2013-07-15 K. Kovarik , T. Jezo , A. Kusina , F. I. Olness , I. Schienbein , T. Stavreva , J. Y. Yu

Electroweak (EW) corrections can be enhanced at high energies due to the soft or collinear radiation of virtual and real $W$ and $Z$ bosons that result in Sudakov-like corrections of the form $\alpha_W^l\log^n(Q^2/M_{W,Z}^2)$, where…

高能物理 - 唯象学 · 物理学 2017-02-10 John M. Campbell , Doreen Wackeroth , Jia Zhou

Uncertainties in the parametrization of Parton Distribution Functions are a serious limiting systematic uncertainty in Large Hadron Collider searches for Beyond the Standard Model physics. This is especially true for measurements at high…

高能物理 - 唯象学 · 物理学 2026-01-27 Yao Fu , Raymond Brock , Daniel Hayden , Chien-Peng Yuan

We present the MMHT2015qed PDF set, resulting from the inclusion of QED corrections to the existing set of MMHT Parton Distribution Functions (PDFs), and which contain the photon PDF of the proton. Adopting an input distribution from the…

高能物理 - 唯象学 · 物理学 2021-05-10 L. A. Harland-Lang , A. D. Martin , R. Nathvani , R. S. Thorne

Vector boson production and neutrino deep-inelastic scattering (DIS) data are crucial for constraining the strange quark parton distribution function (PDF) and more generally for flavor decomposition in PDF extractions. We extend the…

We study the potential impact of inclusive deep-inelastic scattering data from a future electron-ion collider (EIC) on longitudinally polarized parton distribution (PDFs). We perform a PDF determination using the NNPDF methodology, based on…

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

At large values of x the parton distribution functions (PDFs) of the proton are poorly constrained and there are considerable variations between different global fits. Data at such high x have already been published by the ZEUS…

高能物理 - 实验 · 物理学 2020-07-01 ZEUS Collaboration

This document is intended as a study of benchmark cross sections at the LHC (at 7 TeV) at NLO using modern parton distribution functions currently available from the 6 PDF fitting groups that have participated in this exercise. It also…

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

Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation that provides reliable predictive uncertainty in a single forward pass, attracting significant attention. Grounded in subjective logic, EDL derives Dirichlet…

机器学习 · 计算机科学 2024-10-02 Mengyuan Chen , Junyu Gao , Changsheng Xu

Recently a series of new measurements with both the neutral and charge current Drell--Yan processes have been performed at hadron colliders, showing deviations from the predictions of the current parton distribution functions (PDFs). In…

高能物理 - 唯象学 · 物理学 2025-12-09 Zihan Zhao , Minghui Liu , Liang Han

Complete one-loop electroweak corrections to neutral current Drell-Yan process $p p \to \ell^+\ell^- X$ are presented for the case of longitudinal polarization of initial particles. Cross sections for longitudinally polarized protons allow…

高能物理 - 唯象学 · 物理学 2023-05-10 S. Bondarenko , Ya. Dydyshka , L. Kalinovskaya , R. Sadykov , V. Yermolchyk

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 present a detailed numerical study of lepton-pair production via the Drell-Yan process above the Z-peak at the LHC. Our results consistently combine next-to-next-to-leading order QCD corrections and next-to-leading order electroweak…

高能物理 - 唯象学 · 物理学 2014-03-05 Radja Boughezal , Ye Li , Frank Petriello

We present the MCscales approach for incorporating scale uncertainties in parton distribution functions (PDFs). The new methodology builds on the Monte Carlo sampling for propagating experimental uncertainties into the PDF space that…

高能物理 - 唯象学 · 物理学 2023-03-27 Zahari Kassabov , Maria Ubiali , Cameron Voisey

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
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