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相关论文: Precision phenomenology with MCFM

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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…

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

Perturbative quantum chromodynamics (QCD) ceases to be applicable at low interaction energies due to the rapid increase of the strong coupling. In that limit, the non-perturbative regime determines the properties of quarks and gluons…

高能物理 - 唯象学 · 物理学 2021-10-06 Rabah Abdul Khalek

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 present an analysis of parton distribution functions (PDFs) of the proton using Markov Chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes' theorem and thus provides a means to directly sample the underlying…

高能物理 - 唯象学 · 物理学 2026-03-31 Peter Risse , Nasim Derakhshanian , Tomas Jezo , Karol Kovarik , Aleksander Kusina

We determine the uncertainty on the strong coupling alpha_S due to the experimental errors on the data fitted in global analysis of hard-scattering data, within the standard framework of leading-twist fixed-order collinear factorisation in…

高能物理 - 唯象学 · 物理学 2009-11-22 A. D. Martin , W. J. Stirling , R. S. Thorne , G. Watt

We compare double-differential normalized production cross sections for top-antitop $+ X$ hadroproduction at NNLO QCD accuracy, as obtained through a customized version of the MATRIX framework interfaced to PineAPPL, with recent data by the…

高能物理 - 唯象学 · 物理学 2023-12-07 Maria Vittoria Garzelli , Javier Mazzitelli , Sven-Olaf Moch , Oleksandr Zenaiev

Studying the structure of nucleons is not only important to understanding the strong interactions of quarks and gluons, but also to improving the precision of new-physics searches. Since a broad class of experiments, including the LHC and…

高能物理 - 格点 · 物理学 2017-01-02 Huey-Wen Lin

We present MAPPDFpol1.0, a new determination of the helicity-dependent parton distribution functions (PDFs) of the proton from a set of longitudinally polarised inclusive and semi-inclusive deep-inelastic scattering data. The determination…

高能物理 - 唯象学 · 物理学 2025-04-23 MAP , Collaboration , : , Valerio Bertone , Amedeo Chiefa , Emanuele R. Nocera

The determination of theoretical error estimates and PDF/$\alpha_S$-fits require fast evaluations of differential cross sections for varied QCD input parameters. These include PDFs, the strong coupling constant $\alpha_S$ and the…

高能物理 - 唯象学 · 物理学 2015-07-16 Enrico Bothmann , Marek Schönherr , Steffen Schumann

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

高能物理 - 唯象学 · 物理学 2010-11-19 R. S. Thorne

Representing the parton distribution functions (PDFs) of the proton and other hadrons through flexible, high-fidelity parametrizations has been a long-standing goal of particle physics phenomenology. This is particularly true since the…

高能物理 - 唯象学 · 物理学 2024-06-21 Brandon Kriesten , T. J. Hobbs

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

We provide an assessment of the impact of parton distributions on the determination of LHC processes, and of the accuracy with which parton distributions (PDFs) can be extracted from data, in particular from current and forthcoming HERA…

We present a new procedure to determine Parton Distribution Functions (PDFs), based on Markov Chain Monte Carlo (MCMC) methods. The aim of this paper is to show that we can replace the standard $\chi^2$ minimization by procedures grounded…

高能物理 - 唯象学 · 物理学 2017-11-07 Yémalin Gabin Gbedo , Mariane Mangin-Brinet

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,…

We describe the calculation of the next-to-next-to-leading order (NNLO) QCD corrections to isolated photon and photon-plus-jet production, and discuss how the experimental hadron-level photon definition and isolation criteria can be…

高能物理 - 唯象学 · 物理学 2020-04-29 Xuan Chen , Thomas Gehrmann , Nigel Glover , Marius Höfer , Alexander Huss

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

We derive the scheme of NLO computations of generic observables in high-energy hadron-hadron collisions within the framework of high-energy factorization (HEF) with one off-shell initial-state parton, by taking a high-energy limit of the…

高能物理 - 唯象学 · 物理学 2025-02-21 Andreas van Hameren , Maxim Nefedov

Since its start of data taking, the LHC has provided an impressive wealth of information on the quark and gluon structure of the proton. Indeed, modern global analyses of parton distribution functions (PDFs) include a wide range of LHC…

高能物理 - 唯象学 · 物理学 2018-12-05 Rabah Abdul Khalek , Shaun Bailey , Jun Gao , Lucian Harland-Lang , Juan Rojo