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相关论文: Global NLO Analysis of Nuclear Parton Distribution…

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Nuclear parton distribution functions (NPDFs) are determined by global analyses of experimental data on structure-function ratios F_2^A/F_2^{A'} and Drell-Yan cross-section ratios \sigma_{DY}^A/\sigma_{DY}^{A'}. The analyses are done in the…

高能物理 - 唯象学 · 物理学 2008-11-26 M. Hirai , S. Kumano , T. -H. Nagai

We analyze experimental data of nuclear structure-function ratios F_2^A/F_2^{A'} and Drell-Yan cross section ratios for obtaining optimum parton distribution functions (PDFs) in nuclei. Then, uncertainties of the nuclear PDFs are estimated…

高能物理 - 唯象学 · 物理学 2008-11-26 M. Hirai , S. Kumano , T. -H. Nagai

We report global analysis results of experimental data for nuclear structure-function ratios F_2^A/F_2^{A'} and proton-nucleus Drell-Yan cross-section ratios sigma_{DY}^{pA}/sigma_{DY}^{pA'} in order to determine optimum parton distribution…

高能物理 - 唯象学 · 物理学 2009-11-10 M. Hirai , S. Kumano , T. -H. Nagai

We perform a next-to-next-to-leading order (NNLO) analysis of nuclear parton distribution functions (nPDFs) using neutral current charged-lepton ($\ell ^\pm$ + nucleus) deeply inelastic scattering (DIS) data and Drell-Yan (DY) cross-section…

高能物理 - 唯象学 · 物理学 2016-01-29 Hamzeh Khanpour , S. Atashbar Tehrani

Nuclear parton distribution functions (NPDFs) are investigated by analyzing the data on structure functions F_2^A and Drell-Yan cross sections sigma_{DY}^{pA}. An important point of this analysis is to show uncertainties of the NPDFs by the…

高能物理 - 唯象学 · 物理学 2007-05-23 M. Hirai , S. Kumano , T. -H. Nagai

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 report current status of global analyses on nuclear parton distribution functions (NPDFs). The optimum NPDFs are determined by analyzing high-energy nuclear reaction data. Due to limited experimental measurements, antiquark modifications…

高能物理 - 唯象学 · 物理学 2015-05-27 M. Hirai , S. Kumano , K. Saito

Nuclear parton distribution functions are obtained by a chi^2 analysis of lepton deep inelastic experimental data. It is possible to determine valence-quark distributions at medium x and antiquark distributions at small x; however, the…

高能物理 - 唯象学 · 物理学 2017-08-23 S. Kumano

We present a first determination of the nuclear parton distribution functions (nPDF) based on the NNPDF methodology: nNNPDF1.0. This analysis is based on neutral-current deep-inelastic structure function data and is performed up to NNLO in…

高能物理 - 唯象学 · 物理学 2019-07-23 Rabah Abdul Khalek , Jacob J. Ethier , Juan Rojo

In this article we obtain a new set of nuclear parton distribution functions (nuclear PDFs) at next-to-leading order and next-to-next-to-leading order accuracy in perturbative QCD. The common nuclear deep-inelastic scattering (DIS) data…

高能物理 - 唯象学 · 物理学 2021-08-17 Hamzeh Khanpour , Maryam Soleymaninia , S. Atashbar Tehrani , Hubert Spiesberger , Vadim Guzey

We present a next-to-leading order (NLO) global DGLAP analysis of nuclear parton distribution functions (nPDFs) and their uncertainties. Carrying out an NLO nPDF analysis for the first time with three different types of experimental input…

高能物理 - 唯象学 · 物理学 2009-04-22 K. J. Eskola , H. Paukkunen , C. A. Salgado

We analyze experimental data of nuclear structure function ratios $F_2^A/F_2^D$ for obtaining optimum parton distribution functions (PDFs) in nuclei. Then, uncertainties of the nuclear PDFs are estimated by the Hessian method.…

高能物理 - 唯象学 · 物理学 2011-04-11 S. Atashbar Tehrani , A. Mirjalili , Ali N. Khorramian

A QCD analysis of the nuclear parton distributions and structure functions in the NLO is performed by using the world data. By having bounded parton distributions for a nuclear with atomic number A, we can obtain the nuclear structure…

高能物理 - 唯象学 · 物理学 2008-11-26 S. Atashbar Tehrani , Ali N. Khorramian

A brief overview of the global DGLAP analyses of the nuclear parton distribution functions is given. Although all the current global nPDF sets describe $R_{F_2}^A(x,Q^2)$ well in the large-$x$ region where the data exist, variations between…

高能物理 - 唯象学 · 物理学 2015-06-25 V. J. Kolhinen

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

We present a new global QCD analysis of nuclear parton distribution functions and their uncertainties. In addition to the most commonly analyzed data sets for the deep-inelastic scattering of charged leptons off nuclei and Drell-Yan…

高能物理 - 唯象学 · 物理学 2013-05-30 Daniel de Florian , Rodolfo Sassot , Marco Stratmann , Pia Zurita

Parametrization of nuclear parton distributions is investigated in the leading order of alpha_s. The parton distributions are provided at Q^2=1 GeV^2 with a number of parameters, which are determined by a chi^2 analysis of the data on…

高能物理 - 唯象学 · 物理学 2009-11-07 M. Hirai , S. Kumano , M. Miyama

Sets of parton distribution functions (PDFs) of the proton are reported for the leading (LO), next-to-leading (NLO) and next-to-next-to leading order (NNLO) QCD calculations. The parton distribution functions are determined with the…

Study of parton distribution functions (PDFs) has led to a finer cognisance of the structure of partons in hadrons and the proton structure functions in deep inelastic scattering (DIS). PDFs are instrumental in predicting results for most…

高能物理 - 唯象学 · 物理学 2025-10-02 Akbari Jahan , Diptimonta Neog

We calculate nuclear parton distribution functions (PDFs), using the constituent quark model. We find the bounded valon distributions in a nuclear to be related to free valon distributions in a nucleon. By using improved bounded valon…

高能物理 - 唯象学 · 物理学 2009-11-10 S. Atashbar Tehrani , Ali N. Khorramian , A. Mirjalili
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