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PDFs for nuclear targets

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 Added by Karol Kovarik
 Publication date 2010
  fields
and research's language is English
 Authors Karol Kovarik




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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 critically important for any collider experiment with nuclei (e.g. RHIC, ALICE). Here two next-to-leading order chi^2-analyses of NPDF are presented. The first uses neutral current charged-lepton Deeply Inelastic Scattering (DIS) and Drell-Yan data for several nuclear targets and the second uses neutrino-nucleon DIS data. We compare the nuclear corrections factors (F_2^Fe/F_2^D) for the charged-lepton data with other results from the literature. In particular, we compare and contrast fits based upon the charged-lepton DIS data with those using neutrino-nucleon DIS data.



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72 - V.J. Kolhinen 2005
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 their parton distributions can be substantial.
We compare predictions of nCTEQ15 nuclear parton distribution functions with proton-lead vector boson production data from the LHC. We select data sets that are most sensitive to nuclear PDFs and have potential to constrain them. We identify the kinematic regions and flavours where these data can bring new information and will have largest impact on the nuclear PDFs. Finally, we estimate the effect of including these data in a global analysis using a reweighting method.
In this talk, we shortly report results from our recent global DGLAP analysis of nuclear parton distributions. This is an extension of our former EKS98-analysis improved with an automated $chi^2$ minimization procedure and uncertainty estimates. Although our new analysis show no significant deviation from EKS98, a sign of a significantly stronger gluon shadowing could be seen in the RHIC BRAHMS data.
As data become more precise, estimating theoretical uncertainties in global PDF determinations is likely to become increasingly necessary to obtain correspondingly precise PDFs. Here we present a next generation of global proton PDFs (NNPDF4.0) that include theoretical uncertainties due to the use of heavy nuclear and deuteron data in the fit. We estimate these uncertainties by comparing the values of the nuclear observables computed with the nuclear PDFs against those computed with proton PDFs. For heavy nuclear PDFs we use the nuclear nNNPDF2.0 set, while for deuteron PDFs we develop an iterative procedure to determine proton and deuteron PDFs simultaneously, each including the uncertainties in the other. Accounting for nuclear uncertainties resolves some of the tensions in the global fit of the proton PDFs, especially those between the nuclear data and the extended LHC data set used in NNPDF4.0.
In this talk, we introduce our recently completed next-to-leading order (NLO) global analysis of the nuclear parton distribution functions (nPDFs) called EPS09 - a higher order successor to the well-known leading-order (LO) analysis EKS98 and also to our previous LO work EPS08. As an extension to similar global analyses carried out by other groups, we complement the data from deep inelastic $l+A$ scattering and Drell-Yan dilepton measurements in p+$A$ collisions by inclusive midrapidity pion production data from d+Au collisions at RHIC, which results in better constrained gluon distributions than before. The most important new ingredient, however, is the detailed error analysis, which employs the Hessian method and which allows us to map out the parameter-space vicinity of the best-fit to a collection of nPDF error sets. These error sets provide the end-user a way to compute how the PDF-uncertainties will propagate into the cross sections of his/her interest. The EPS09 package to be released soon, will contain both the NLO and LO results for the best fits and the uncertainty sets.
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