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Blümlein, J.; Böttcher, H.; Guffanti, A.

doi:10.1016/j.nuclphysb.2007.03.035

Cited by 184 publications

(349 citation statements)

References 84 publications

(139 reference statements)

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“…Including them nevertheless in a standard NNLO analysis requires [14,84] generally larger values of α s (M 2 Z ). Previous standard NNLO fits (Q 0 > 1 GeV) considering only the flavor non-singlet (NS) valence sector of structure functions [85,86,87] Table 2 but remain within a 1σ − 2σ uncertainty. A similar NS valence analysis [88] as well as a full analysis [89] being based, however, on incomplete calculations of the moments of 3-loop anomalous dimensions (splitting functions) yielded slightly larger values of α s at NNLO,…”

Section: Quantitative Results and Dynamical Small-x Predictionsmentioning

confidence: 85%

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Dynamical next-to-next-to-leading order parton distributions

2009

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Utilizing recent DIS measurements (σ r , F 2,3,L ) and data on hadronic dilepton production we determine at NNLO (3-loop) of QCD the dynamical parton distributions of the nucleon generated radiatively from valencelike positive input distributions at an optimally chosen low resolution scale (Q 2 0 < 1 GeV 2 ). These are compared with 'standard' NNLO distributions generated from positive input distributions at some fixed and higher resolution scale (Q 2 0 > 1 GeV 2 ). Although the NNLO corrections imply in both approaches an improved value of χ 2 , typically χ 2 NNLO 0.9χ 2 NLO , present DIS data are still not sufficiently accurate to distinguish between NLO results and the minute NNLO effects of a few percent, despite of the fact that the dynamical NNLO uncertainties are somewhat smaller than the NLO ones and both are, as expected, smaller than those of their 'standard' counterparts. The dynamical predictions for F L (x, Q 2 ) become perturbatively stable already at Q 2 = 2 − 3 GeV 2 where precision measurements could even delineate NNLO effects in the very small-x region. This is in contrast to the common 'standard' approach but NNLO/NLO differences are here less distinguishable due to the larger 1σ uncertainty bands. Within the dynamical approach we obtain α s (M 2 Z ) = 0.1124 ± 0.0020, whereas the somewhat less constrained 'standard' fit gives α s (M 2 Z ) = 0.1158 ± 0.0035.arXiv:0810.4274v2 [hep-ph]

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Section: Quantitative Results and Dynamical Small-x Predictionsmentioning

confidence: 85%

“…Our standard NLO results [8] are shown by the dotted curves. For comparison the standard NNLO results of AMP06 [17] and BBG06 [87] are shown as well. Our dynamical valence distributions at Q 2 = 4 GeV 2 practically coincide with the standard ones shown.…”

Section: Discussionmentioning

confidence: 99%

Dynamical next-to-next-to-leading order parton distributions

2009

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“…Finally, it is interesting to compare our NNLO determination of the nonsinglet parton distribution to the results of some recent NNLO fits to nonsinglet DIS data [35,36] figure 17). Results are analogous to those found when comparing to global fits.…”

Section: Comparison To Data and Other Parton Fitsmentioning

confidence: 93%

“…No variation of the quality of the fit is found between different perturbative orders: the values of χ 2 and σ (net) dat are unaffected by the perturbative order at which the computation is performed. Indeed, only very small improvements of the χ 2 when going from LO to NLO and NNLO have been found in recent nonsinglet fits [35,36].…”

Section: Higher Orders and The Value Of The Strong Couplingmentioning

confidence: 97%

Neural network determination of parton distributions: the nonsinglet case

Collaboration¹,

Debbio²,

Forte³

et al. 2007

J. High Energy Phys.

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Abstract:We provide a determination of the isotriplet quark distribution from available deep-inelastic data using neural networks. We give a general introduction to the neural network approach to parton distributions, which provides a solution to the problem of constructing a faithful and unbiased probability distribution of parton densities based on available experimental information. We discuss in detail the techniques which are necessary in order to construct a Monte Carlo representation of the data, to construct and evolve neural parton distributions, and to train them in such a way that the correct statistical features of the data are reproduced. We present the results of the application of this method to the determination of the nonsinglet quark distribution up to next-to-next-to-leading order, and compare them with those obtained using other approaches.

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“…These corrections include, in particular, resummation of threshold logarithms, Target Mass Corrections (TMCs), higher-twist diagrams, and nuclear corrections when nuclear targets are considered. The last three have been included consistently, for example, in the CTEQ-JLab collaboration PDF fits [6] and the fits by Alekhin and collaborators [13], allowing one to substantially extend the range in x of the fitted DIS data (see also [14] for the interplay of TMC, higher-twists and higher order perturbative corrections). Threshold resummation, however, has been considered so far only to estimate the theoretical errors of PDFs or used in fits of only a subset of the data [15][16][17][18], but has not yet been fully included for all relevant data sets in a global QCD fit.…”

Section: Introductionmentioning

confidence: 99%

Interplay of threshold resummation and hadron mass corrections in deep inelastic processes

2015

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We discuss hadron mass corrections and threshold resummation for deep-inelastic scattering ℓN → ℓ ′ X and semi-inclusive annihilation e + e − → hX processes, and provide a prescription how to consistently combine these two corrections respecting all kinematic thresholds. We find an interesting interplay between threshold resummation and target mass corrections for deep-inelastic scattering at large values of Bjorken xB. In semi-inclusive annihilation, on the contrary, the two considered corrections are relevant in different kinematic regions and do not affect each other. A detailed analysis is nonetheless of interest in the light of recent high precision data from BaBar and Belle on pion and kaon production, with which we compare our calculations. For both deep inelastic scattering and single inclusive annihilation, the size of the combined corrections compared to the precision of world data is shown to be large. Therefore, we conclude that these theoretical corrections are relevant for global QCD fits in order to extract precise parton distributions at large Bjorken xB, and fragmentation functions over the whole kinematic range.

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Non-singlet QCD analysis of deep inelastic world data at O(αs3)

Cited by 184 publications

References 84 publications

Dynamical next-to-next-to-leading order parton distributions

Dynamical next-to-next-to-leading order parton distributions

Neural network determination of parton distributions: the nonsinglet case

Interplay of threshold resummation and hadron mass corrections in deep inelastic processes

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