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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 present a new analysis to extract pion's parton distribution functions (PDFs) in the framework of the statistical model. Starting from the statistical model framework first developed for the spin-1/2 nucleon, we apply appropriate…

高能物理 - 唯象学 · 物理学 2021-02-03 Claude Bourrely , Franco Buccella , Jen-Chieh Peng

We present an updated global analysis of collinearly factorized nuclear parton distribution functions (PDFs) at next-to-leading order in perturbative QCD. In comparison to our previous fit, EPPS16, the present analysis includes more data…

高能物理 - 唯象学 · 物理学 2022-05-25 Kari J. Eskola , Petja Paakkinen , Hannu Paukkunen , Carlos A. Salgado

The need for accurate and precise polarised parton distribution functions (PDFs) is becoming increasingly crucial in view of the Electron-Ion Collider experimental program foreseen in the coming years. Two global PDF determinations at…

高能物理 - 唯象学 · 物理学 2024-09-17 Amedeo Chiefa

We extract new parton distribution functions (PDFs) of the proton by global analysis of hard scattering data in the general-mass framework of perturbative quantum chromodynamics. Our analysis includes new theoretical developments together…

高能物理 - 唯象学 · 物理学 2010-10-27 Hung-Liang Lai , Marco Guzzi , Joey Huston , Zhao Li , Pavel M. Nadolsky , Jon Pumplin , C. -P. Yuan

We propose a new experimental method to probe the photon parton distribution function inside the proton (photon PDF) at LHC energies. The method is based on the measurement of dilepton production from the $\gamma p\rightarrow\ell^+\ell^-+X$…

高能物理 - 唯象学 · 物理学 2019-06-19 Mateusz Dyndal , Alexander Glazov , Marta Luszczak , Renat Sadykov

In this contribution we discuss the main outcomes of our studies on the so called double parton distribution functions (dPDFs), accessible quantities in high energy proton-proton and proton nucleus collisions, in double parton scattering…

高能物理 - 唯象学 · 物理学 2020-01-08 Matteo Rinaldi

The xPDFsuite software program is described. It is for processing and analyzing atomic pair distribution functions (PDF) from X-ray powder diffraction data. It provides a convenient GUI for SrXplanr and PDFgetX3, allowing the users to…

材料科学 · 物理学 2015-02-25 Xiaohao Yang , Pavol Juhas , Christopher L. Farrow , Simon J. L. Billinge

We present a determination of the parton distributions of the nucleon from a global set of hard scattering data using the NNPDF methodology at LO and NNLO in perturbative QCD, thereby generalizing to these orders the NNPDF2.1 NLO parton…

A method to facilitate the consistent inclusion of cross-section measurements based on complex final-states from HERA, TEVATRON and the LHC in proton parton density function (PDF) fits has been developed. This can be used to increase the…

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

Given the non-negligible interplay between parton distribution functions (PDFs) at large x and potential New Physics (NP) effects in the high-energy tails of hadron collider observables, a central question is which PDFs can be reliably…

高能物理 - 唯象学 · 物理学 2026-02-25 Ella Cole , Mark N. Costantini , Elie Hammou , Luca Mantani , Francesco Merlotti , Manuel Morales-Alvarado , Maria Ubiali

Collinear parton distribution functions (cPDFs) and transverse momentum dependent distributions (TMDs) are essential for calculating cross sections in high-energy physics, particularly within collinear and kt-factorization frameworks.…

高能物理 - 唯象学 · 物理学 2026-02-16 R. Kord Valeshabadi , S. Rezaie

Vector meson production in ultra-peripheral pA and AA collisions at the CERN Large Hadron Collider (LHC) are very sensitive to Parton Distribution Functions (PDF) as well as to their leading-order, next-to-leading-order, and medium…

高能物理 - 唯象学 · 物理学 2016-03-08 J. Thomas , C. A. Bertulani , N. Brady , D. B. Clark , E. Godat , F. Olness

We summarize recent progress toward the next generation of CTEQ-TEA parton distribution functions, CT25, based on a global NNLO analysis that incorporates a significant sample of newly included LHC data. We present a baseline fit within the…

高能物理 - 唯象学 · 物理学 2025-12-30 A. Ablat , A. Courtoy , S. Dulat , Y. Fu , M. Guzzi , T. J. Hobbs , J. Huston , K. Mohan , P. Nadolsky , M. Ponce-Chavez , D. Stump , K. Xie , C. -P. Yuan

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…

We perform the next-to-next-leading-order (NNLO) QCD global fit of PDFs using inclusive charged-lepton and neutrino DIS data down to Q = 1 GeV. We also consider the data on neutrino-nucleon dimuon production, that allows us to disentangle…

高能物理 - 唯象学 · 物理学 2008-10-28 S. Alekhin , S. Kulagin , R. Petti

Parton distribution functions play a pivotal role in hadron collider phenomenology. They are non-perturbative quantities extracted from fits to available data, and their scale dependence is dictated by the DGLAP evolution equations. In this…

Parton distribution functions (PDFs) describe the structure of hadrons as composed of quarks and gluons. They are needed to make predictions for short-distance processes in high-energy collisions and are determined by fitting to cross…

高能物理 - 唯象学 · 物理学 2020-11-11 Karol Kovarik , Pavel M. Nadolsky , Davison E. Soper

Quantitatively connecting properties of parton distribution functions (PDFs, or parton densities) to the theoretical assumptions made within the QCD analyses which produce them has been a longstanding problem in HEP phenomenology. To…

高能物理 - 唯象学 · 物理学 2024-07-08 Brandon Kriesten , Jonathan Gomprecht , T. J. Hobbs