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相关论文: Precision phenomenology with MCFM

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We present a detailed comparison of the most recent sets of NNLO PDFs from the ABM, CT, HERAPDF, MSTW and NNPDF collaborations. We compare parton distributions at low and high scales and parton luminosities relevant for LHC phenomenology.…

High Energy collider experiments are moving to the highest precision frontier quickly. The predictions of observables are based on the factorization formula which helps to connect small to large distances. These predictions can be…

高能物理 - 唯象学 · 物理学 2023-05-29 David F. Renteria-Estrada , Roger J. Hernandez-Pinto , G. F. R. Sborlini , Pia Zurita

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

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

In high-energy particle physics, extracting parton distribution functions (PDFs) from lattice quantum chromodynamics (QCD) calculations remains a significant challenge, particularly due to the divergent nature of perturbative expansions at…

高能物理 - 唯象学 · 物理学 2024-11-26 Jia-Lu Zhang

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

The parton distributions functions (PDFs) derived from the NNLO QCD analysis of existing light-targets deep-inelastic-scattering data are presented. The NLO and NNLO PDFs are compared in order to analyze perturbative stability of the…

高能物理 - 唯象学 · 物理学 2007-05-23 S. Alekhin

We present a method for very fast repeated computations of higher-order cross sections in hadron-induced processes for arbitrary parton density functions. A full implementation of the method for computations of jet cross sections in…

高能物理 - 唯象学 · 物理学 2017-08-23 T. Kluge , K. Rabbertz , M. Wobisch

I consider variations in the definitions, at next-to-leading order (NLO) and at next-to-next-to leading order (NNLO), of a General-Mass Variable Flavour Number Scheme (GM-VFNS) for heavy flavour structure functions. I also define a new…

高能物理 - 唯象学 · 物理学 2012-11-12 R. S. Thorne

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 present the latest results of studies within the MMHT PDF framework. We discuss the impact of the most recent ATLAS 7 TeV jet data, demonstrating that while a good fit can be achieved for individual jet rapidity bins, it is not possible…

高能物理 - 唯象学 · 物理学 2017-08-02 L. A. Harland-Lang , A. D. Martin , R. Nathvani , R. S. Thorne

We present new parton distribution functions (PDFs) at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) in perturbative QCD, derived from a comprehensive global QCD analysis of high-precision data sets from combined HERA…

高能物理 - 唯象学 · 物理学 2024-12-17 Majid Azizi , Maryam Soleymaninia , Hadi Hashamipour , Maral Salajegheh , Hamzeh Khanpour , Ulf-G. Meißner

We present the first unbiased determination of parton distribution functions (PDFs) with electroweak corrections. The aim of this thesis is to provide an exhaustive description of the theoretical framework and the technical implementation…

高能物理 - 唯象学 · 物理学 2015-09-03 Stefano Carrazza

The potential of the LHeC, a future electron-proton collider, for precision Deep Inelastic Scattering measurements is reviewed with particular emphasis on the reduction of uncertainties on the parton distribution functions (PDFs) of the…

高能物理 - 唯象学 · 物理学 2019-08-14 A. M. Cooper-Sarkar

We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel…

We summarise recent developments in the path towards the "MMHT19" parton distribution functions. We concentrate on the extraction of the strange quark upon the improvement of theoretical calculations for NNLO charged current cross sections;…

高能物理 - 唯象学 · 物理学 2019-07-19 R. S. Thorne , S. Bailey , T. Cridge , L. A. Harland-Lang , A. D. Martin , R. Nathvani

We present a new QCD analysis of parton distribution functions (PDFs) at the next-to-leading order (NLO) and next-to-next-to-leading order (NNLO). In the present paper, the role of special types of experimental measurements for extracting…

高能物理 - 唯象学 · 物理学 2024-05-20 Majid Azizi , Ali Khorramian , Saeid Paktinat Mehdiabadi

Jet production at hadron colliders provides powerful constraints on the parton distribution functions (PDFs) of the proton, in particular on the gluon PDF. Jet production can also be used to extract the QCD coupling constant and to test its…

高能物理 - 唯象学 · 物理学 2015-10-13 Juan Rojo

We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering…

Monte Carlo event generators for hard hadronic collisions depend on the evolution of parton showers backwards from a high-scale subprocess to the hadronization scale. The evolution is treated as a branching process with a sequence of…

高能物理 - 唯象学 · 物理学 2024-04-16 Stefano Frixione , Bryan R. Webber