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The nuclear parton distribution functions (nPDFs) of gluons are known to be difficult to determine with fits of deep inelastic scattering (DIS) and Drell-Yan (DY) data alone. Therefore, the nCTEQ15 analysis of nuclear PDFs added inclusive…

高能物理 - 唯象学 · 物理学 2021-08-02 P. Duwentäster , L. A. Husová , T. Ježo , M. Klasen , K. Kovařík , A. Kusina , K. F. Muzakka , F. I. Olness , I. Schienbein , J. Y. Yu

A clear understanding of nuclear parton distribution functions (nPDFs) plays a crucial role in the interpretation of collider data taken at the Relativistic Heavy Ion Collider (RHIC), the Large Hadron Collider (LHC) and in the near future…

高能物理 - 唯象学 · 物理学 2022-07-13 P. Duwentäster , T. Ježo , M. Klasen , K. Kovařík , A. Kusina , K. F. Muzakka , F. I. Olness , R. Ruiz , I. Schienbein , J. Y. Yu

We present the nCTEQ15 global analysis of nuclear parton distribution functions (nPDFs). The main addition to the previous nCTEQ PDFs is the introduction of PDF uncertainties based on the Hessian method. Another important improvement is the…

高能物理 - 唯象学 · 物理学 2016-11-04 A. Kusina

Vector boson production and neutrino deep-inelastic scattering (DIS) data are crucial for constraining the strange quark parton distribution function (PDF) and more generally for flavor decomposition in PDF extractions. We extend the…

We present an improved leading-order global DGLAP analysis of nuclear parton distribution functions (nPDFs), supplementing the traditionally used data from deep inelastic lepton-nucleus scattering and Drell-Yan dilepton production in…

高能物理 - 唯象学 · 物理学 2010-03-25 K. J. Eskola , H. Paukkunen , C. A. Salgado

Understanding nuclear effects in parton distribution functions (PDF) is an essential component needed to determine the strange and anti-strange quark contributions in the proton. In addition Nuclear Parton Distribution Functions (NPDF) are…

高能物理 - 唯象学 · 物理学 2010-11-19 Karol Kovarik

As the current nuclear PDF analyses are mainly constrained by fixed-target Drell-Yan and deeply inelastic scattering data only the quark nuclear modifications at fairly large $x$ values are in a good control. Inclusive pion production in…

高能物理 - 唯象学 · 物理学 2015-10-02 Ilkka Helenius , Hannu Paukkunen , Kari J. Eskola

The extraction of the strange quark parton distribution function (PDF) poses a long-standing puzzle. Measurements from neutrino-nucleus deep inelastic scattering (DIS) experiments suggest the strange quark is suppressed compared to the…

The NNPDF collaboration has recently presented NNPDF3.1, a new determination of the parton distribution functions (PDFs) of the proton including a number of new data, some of which are particularly sensitive to the gluon PDF at large x. In…

高能物理 - 唯象学 · 物理学 2019-02-25 Emanuele R. Nocera , Maria Ubiali

We introduce a global analysis of collinearly factorized nuclear parton distribution functions (PDFs) including, for the first time, data constraints from LHC proton-lead collisions. In comparison to our previous analysis, EPS09, where data…

高能物理 - 唯象学 · 物理学 2017-04-05 Kari J. Eskola , Petja Paakkinen , Hannu Paukkunen , Carlos A. Salgado

We present the new nCTEQ15 set of nuclear parton distribution functions with uncertainties. This fit extends the CTEQ proton PDFs to include the nuclear dependence using data on nuclei all the way up to 208^Pb. The uncertainties are…

高能物理 - 唯象学 · 物理学 2016-05-04 K. Kovarik , A. Kusina , T. Jezo , D. B. Clark , C. Keppel , F. Lyonnet , J. G. Morfin , F. I. Olness , J. F. Owens , I. Schienbein , J. Y. Yu

This work studies collinearly factorizable nuclear parton distribution functions (nPDFs) in perturbative Quantum Chromodynamics (QCD) at next-to-leading order in the light of hadron-nucleus collision data which have not been included in…

高能物理 - 唯象学 · 物理学 2019-12-18 Petja Paakkinen

We use the nCTEQ analysis framework to investigate nuclear Parton Distribution Functions (nPDFs) in the region of large x and intermediate-to-low $Q$, with special attention to recent JLab Deep Inelastic Scattering data on nuclear targets.…

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 discuss the two most recent global analyses of nuclear parton distribution functions within the nCTEQ approach. LHC data on $W/Z$-boson, single-inclusive hadron and heavy quark/quarkonium production are shown to not only significantly…

高能物理 - 唯象学 · 物理学 2022-10-20 M. Klasen , P. Duwentäster , T. Jezo , K. Kovarik , A. Kusina , J. G. Morfin , K. F. Muzakka , F. I. Olness , R. Ruiz , I. Schienbein , J. Y. Yu

In global analyses of nuclear parton distribution functions (nPDFs), neutrino deep-inelastic scattering (DIS) data have been argued to exhibit tensions with the data from charged-lepton DIS. Using the nCTEQ framework, we investigate these…

A study is presented of the impact of simulated inclusive Electron Ion Collider Deep Inelastic Scattering data on the determination of the proton and nuclear parton distribution functions (PDFs) at next-to-next-to-leading and…

The interest into parton distribution functions (PDFs) and fragmentation functions (FFs) in current high energy physics research is twofold. On the one hand, they are fundamental objects to conduct precision phenomenology studies, e.g. at…

高能物理 - 唯象学 · 物理学 2025-09-22 Tanishq Sharma

We present the first official release of the nCTEQ nuclear parton distribution functions (nPDFs) with errors. The main addition to the previous nCTEQ PDFs is the introduction of PDF uncertainties based on the Hessian method. Another…

高能物理 - 唯象学 · 物理学 2016-06-27 A. Kusina

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
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