Measurement of the proton and deuteron structure functions, F2 and F2, and of the ratio

Arneodo, M.; Arvidson, Adam Regn; Badełek, B.; Ballintijn, M.; Baum, G.; Beaufays, J.; Bird, I.; Björkholm, P.; Botje, M.; Broggini, C.; Brückner, W.; Brüll, A.; Burger, W. J.; Ciborowski, J.; Dantzig, R. van; Dyring, A.; Engelien, H.; Ferrero, M. I.; Fluri, L.; Gaul, U.; Granier, T.; Grosse‐Perdekamp, M.; Harrach, D. von; Heijden, M. van der; Heusch, C. A.; Ingram, Q.; Jong, M. de; Kabuß, E. M.; Kaiser, R.; Ketel, T.; Klein, Frank; Kullander, S.; Landgraf, U.; Lindqvist, T.; Mallot, G.K.; Mariotti, C.; Middelkoop, G. van; Milsztajn, A.; Mizuno, Y.; Mücklich, A.; Nassalski, J.; Nowotny, D.; Oberski, J.E.J.; Paić, A.; Peroni, C.; Povh, Bogdan; Prytz, Kjell; Rieger, R.; Rith, Klaus; Röhrich, K.; Rondio, E.; Ropelewski, L.; Sandacz, A.; Sanders, D. A.; Scholz, Christoph; Seitz, R.; Sever, F.; Shibata, T. A.; Siebler, Mario; Simon, A.; Staiano, A.; Szleper, M.; Tłaczała, W.; Tzamouranis, Y.; Virchaux, M.; Vuilleumier, J. L.; Walcher, Th.; Windmolders, R.; Witzmann, A.; Заремба, К.; Zetsche, Frank

doi:10.1016/s0550-3213(96)00538-x

Cited by 384 publications

(198 citation statements)

References 53 publications

(13 reference statements)

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“…These free parameters have been fixed using the following data sets: the HERA ep measurements [70,71,72,73,74] for Q 2 ≥ 2 GeV 2 for the 'reduced' cross sections σ r and σ r,NC in (12); the fixed target F p 2 data of SLAC [75], BCDMS [76], E665 [77] and NMC [78], subject to the standard cuts Q 2 ≥ 4 GeV 2 and W 2 = Q 2 ( 1 x − 1) + m 2 p ≥ 10 GeV 2 , together with the structure function ratios F n 2 /F p 2 of BCDMS [79], E665 [80] and NMC [81]. Furthermore the Drell-Yan muon pair production data of E866/NuSea [82] for…”

Section: Quantitative Results and Dynamical Small-x Predictionsmentioning

confidence: 99%

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: 99%

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

2009

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“…In short, early SLAC data have extremely large uncertainties, while data from JLAB and the E665 collaboration [17] are mostly at low Q 2 and are almost entirely excluded by our kinematic cuts. This leaves us with the data published by the NMC [18] and BCDMS [19,20] collaborations. We refer to [9] for a discussion of the kinematical and statistical features of these data, in particular for the details of their correlated systematics.…”

Section: Experimental Data and Kinematical Cutsmentioning

confidence: 93%

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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“…The reference fit was made to data from the BCDMS Collaboration [10] for F p 2 and F d 2 , from the NMC Collaboration [11] for F p 2 and F d 2 /F p 2 , from H1 [12] and ZEUS [13] for F P 2 , from CDF [14] and DØ [15] for inclusive jet production, from CDF [16] for the W lepton asymmetry, from E-866 [17] for the ratio of lepton pair cross sections for pd and pp interac-tions, and from E-605 [18] for dimuon production in pN interactions. Note that the E-605 data were taken on a copper target, but the nuclear corrections in the relevant kinematic region have been measured [20] to be consistent with A 1 , so no nuclear corrections were included for these data.…”

Section: Global Fitsmentioning

confidence: 99%

“…The data for F d 2 /F p 2 integrated over Q 2 [11] have proven to be instrumental in constraining the d/u ratio in the fits discussed in the text. These data are shown in Figure 7 along with several curves described in the text.…”

Section: Global Fitsmentioning

confidence: 99%

Impact of new neutrino deep inelastic scattering and Drell-Yan data on large-xparton distributions

Owens¹,

Huston²,

Keppel

et al. 2007

Phys. Rev. D

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New data sets have recently become available for neutrino and antineutrino deep inelastic scattering on nuclear targets and for inclusive dimuon production in pp and pd interactions. These data sets are sensitive to different combinations of parton distribution functions in the large-x region and, therefore, provide different constraints when incorporated into global parton distribution function fits. We compare and contrast the effects of these new data on parton distribution fits, with special emphasis on the effects at large x. The effects of the use of nuclear targets in the neutrino and antineutrino data sets are also investigated.

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Measurement of the proton and deuteron structure functions, F2 and F2, and of the ratio

Cited by 384 publications

References 53 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

Impact of new neutrino deep inelastic scattering and Drell-Yan data on large-xparton distributions

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