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

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In this talk, we present the results from our recent global reanalysis of nuclear parton distribution functions (nPDFs), where the DGLAP-evolving nPDFs are constrained by nuclear hard process data from deep inelastic $l+A$ scattering (DIS)…

高能物理 - 唯象学 · 物理学 2008-11-26 K. J. Eskola , V. J. Kolhinen , H. Paukkunen , C. A. Salgado

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

Parton Distribution Functions (PDFs) are a key ingredient in theoretical predictions for Large Hadron Collider (LHC) observables and play a central role in the extraction of precision Standard Model (SM) and Beyond the SM (BSM) parameters…

We present Fanto10, a new ensemble of NLO error parton distribution functions (PDFs) in a charged pion that provides the most detailed estimate of uncertainties from experimental, theoretical, and methodological sources in order to enable…

高能物理 - 唯象学 · 物理学 2025-05-21 Lucas Kotz , Aurore Courtoy , Pavel Nadolsky , Maximiliano Ponce-Chavez

We have presented the results of our next-to-next-to-leading order (NNLO) QCD analysis of nuclear parton distribution functions (nuclear PDFs) [Phys. Rev. D 93 (2016) 014026, arXiv:1601.00939 [hep-ph]] using all available neutral current…

高能物理 - 唯象学 · 物理学 2020-12-17 S. Atashbar Tehrani

We evaluate the uncertainties due to nuclear effects in global fits of proton parton distribution functions (PDFs) that utilise deep-inelastic scattering and Drell-Yan data on deuterium targets. To do this we use an iterative procedure to…

高能物理 - 唯象学 · 物理学 2021-02-03 Richard D. Ball , Emanuele R. Nocera , Rosalyn L. Pearson

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

高能物理 - 唯象学 · 物理学 2022-06-09 Ilkka Helenius , Marina Walt , Werner Vogelsang

We present the first unbiased determination of spin-dependent, or polarized, Parton Distribution Functions (PDFs) of the proton. A statistically sound representation of the corresponding uncertainties is achieved by means of the NNPDF…

高能物理 - 唯象学 · 物理学 2014-03-04 Emanuele Roberto Nocera

We present an updated determination of nuclear parton distributions (nPDFs) from a global NLO QCD analysis of hard processes in fixed-target lepton-nucleus and proton-nucleus together with collider proton-nucleus experiments. In addition to…

高能物理 - 唯象学 · 物理学 2022-05-27 Rabah Abdul Khalek , Rhorry Gauld , Tommaso Giani , Emanuele R. Nocera , Tanjona R. Rabemananjara , Juan Rojo

We formulate a general approach to the inclusion of theoretical uncertainties, specifically those related to the missing higher order uncertainty (MHOU), in the determination of parton distribution functions (PDFs). We demonstrate how,…

The production of lepton pairs (Drell-Yan process) at the LHC is being measured to high precision, enabling the extraction of distributions that are triply differential in the di-lepton mass and rapidity as well as in the scattering angle…

高能物理 - 唯象学 · 物理学 2023-08-09 A. Gehrmann-De Ridder , T. Gehrmann , E. W. N. Glover , A. Huss , C. T. Preuss , D. M. Walker

We review recent progress in the determination of the parton distribution functions (PDFs) of the proton, with emphasis on the applications for precision phenomenology at the Large Hadron Collider (LHC). First of all, we introduce the…

高能物理 - 唯象学 · 物理学 2018-05-23 Jun Gao , Lucian Harland-Lang , Juan Rojo

I present a determination of longitudinally-polarized parton distribution functions of the proton from inclusive deep-inelastic scattering data: NNPDFpol1.0+. This determination, based on the NNPDF methodology, upgrades a previous analysis,…

高能物理 - 唯象学 · 物理学 2016-02-17 Emanuele R. Nocera

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

We present a comparative study of the invariant mass and rapidity distributions in Drell-Yan lepton pair production, with particular emphasis on the role played by the QCD evolution. We focus our study around the Z resonance ($50 <Q < 200$…

高能物理 - 唯象学 · 物理学 2010-10-27 Alessandro Cafarella , Claudio Coriano , Marco Guzzi

Parton Distribution Functions (PDFs) contribute significantly to the uncertainty on the determination of the top-quark pole mass and other precision measurements at the Large Hadron Collider (LHC). It is crucial to understand these…

高能物理 - 唯象学 · 物理学 2024-01-25 Jason P. Gombas , Reinhard Schwienhorst , Binbin Dong , Jarrett Fein

We report on the first complete computation of the mixed QCD$-$electroweak (EW) corrections to the neutral-current Drell$-$Yan process. Superseding previously applied approximations, our calculation provides the first result at this order…

A selection of the latest and most frequently used parton distribution functions (PDFs) is incorporated in Pythia8, including the Monte Carlo-adapted PDFs from the MSTW and CTEQ collaborations. This article examines the differences in PDFs…

高能物理 - 唯象学 · 物理学 2010-11-19 Tomas Kasemets , Torbjörn Sjöstrand

We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code…

A recent study by Wang {\it et al.}(arXiv:2309.01417) proposed a novel connection between the nature of the parton distribution function (PDF) and the characteristics of its moments. In this study, we apply these findings to analyze the…

高能物理 - 唯象学 · 物理学 2024-07-16 Xiaobin Wang , Zexin Wu , Minghui Ding , Lei Chang