2006
DOI: 10.1016/j.nuclphysb.2006.05.010
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NNLO logarithmic expansions and exact solutions of the DGLAP equations from x-space: New algorithms for precision studies at the LHC

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Cited by 50 publications
(40 citation statements)
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“…At higher order, structure functions are obtained by convoluting the quark and gluon distributions with the corresponding pertuabative coefficient functions. Having the next-to-next-to-leading order (NNLO) contributions of the quark-quark and gluonquark splitting functions in perturbative QCD [41], one can obtain the evolution of longitudinally polarized parton densities of hadrons up to NNLO order of QCD [42]. The NNLO spin-dependent proton structure functions, g p 1 (x, Q 2 ), can be written as a linear combination of po-larized parton distribution functions ∆q, ∆q and ∆g as,…”
Section: Polarized Pdfs Analysis Methodsmentioning
confidence: 99%
“…At higher order, structure functions are obtained by convoluting the quark and gluon distributions with the corresponding pertuabative coefficient functions. Having the next-to-next-to-leading order (NNLO) contributions of the quark-quark and gluonquark splitting functions in perturbative QCD [41], one can obtain the evolution of longitudinally polarized parton densities of hadrons up to NNLO order of QCD [42]. The NNLO spin-dependent proton structure functions, g p 1 (x, Q 2 ), can be written as a linear combination of po-larized parton distribution functions ∆q, ∆q and ∆g as,…”
Section: Polarized Pdfs Analysis Methodsmentioning
confidence: 99%
“…2 Several techniques to accomplish a numerical solution of these integro-differential equations have been developed both in x-space [7,8], and Mellin space [9]. …”
Section: The Techniquementioning
confidence: 99%
“…A C (x) = (P 0 − M 0 ) + (P 1 − M 1 ) · Log (Log(x)) (P 0 + M 0 ) + (P 1 + M 1 ) · Log (Log(x)) (A.11) with dA C (x) dx = 2 · (P 1 M 0 − P 0 M 1 ) x · Log(x) · [(P 0 + M 0 ) + (P 1 + M 1 ) · Log (Log(x))] 2 (A. 12) and, since x > 0 (in practice x > 10 GeV) and Log(x) > 0,…”
Section: A22 Polynomials Of Log (Log(x))mentioning
confidence: 99%