A high statistics measurement of the proton structure functions F2(x, Q2) and R from deep inelastic muon scattering at high Q2

Benvenuti, A. C.; Bollini, D.; Bruni, G.; Camporesi, T.; Monari, L.; Navarria, F. L.; Argento, A.; Cvach, J.; Lohmann, W.; Piemontese, L.; Závada, P.; Akhundov, A.A.; Genchev, V.; Golutvin, I.; Kiryushin, Yu.T.; Krivokhizhin, V.G.; Kukhtin, V.; Lednický, R.; Němeček, S.; Reimer, P.; Savin, I.; Smirnov, G.I.; Strachota, J.; Sultanov, G.; Todorov, Plamen; Volodko, A.; Jamnik, D.; Kopp, R.; Meyer‐Berkhout, U.; Staude, A.; Teichert, K. M.; Tirler, R.; Voss, R.; Zupančič, Č.; Cribier, M.; Feltesse, J.; Milsztajn, A.; Ouraou, A.; Rich-Hennion, P.; Sacquin, Y.; Smadja, G.; Virchaux, M.

doi:10.1016/0370-2693(89)91637-7

Cited by 505 publications

(199 citation statements)

References 118 publications

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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: 94%

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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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Section: Experimental Data and Kinematical Cutsmentioning

confidence: 94%

“…(B.16), but with the average value eq. (B.17) replaced by the variance 19) and similarly for the covariance. Note that the distance is just the square root of the sample variance normalized to the variance of which it is an unbiased estimator (see ref.…”

Section: Jhep03(2007)039mentioning

confidence: 99%

Neural network determination of parton distributions: the nonsinglet case

Collaboration¹,

Debbio²,

Forte³

et al. 2007

J. High Energy Phys.

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“…The full error bars represent the statistical and systematic errors added in quadrature. The error band represents the uncertainty of the calculation of F L using the gluon and quark distributions, as determined from the NLO QCD analysis of the H1 data [6] for y 0:35 and the BCDMS data [8]. The dashed lines de ne the allowed range of F L values from F L = 0 to F L = F 2 where F 2 is given by the QCD t.…”

Section: Discussionmentioning

confidence: 99%

“…The t used the H1 data [6] for y < 0 : 35. The BCDMS proton and deuterium data [8] were used to constrain the high x behaviour of the parton distributions. In contrast with the previous QCD analysis performed by H1 [6], the NMC data [20] were not included in the standard t to ensure a maximum weight of the H1 data in the t procedure.…”

Section: Qcd Fitmentioning

confidence: 99%

“…The following procedure is applied. Our measurement o f F 2 [6], for y < 0 : 35, and xed target data at larger x [8] are used to extract the parton distribution functions which are evolved in Q 2 according to the NLO DGLAP evolution equations. This provides predictions for the structure function F 2 in the high y region which allow, by subtraction of the contribution of F 2 to the DIS cross section (cf.…”

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confidence: 99%

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Determination of the longitudinal proton structure function FL(x,Q2) at low x

Adloff¹,

Aïd²,

Anderson³

et al. 1997

Physics Letters B

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Abstract:A measurement of the inclusive cross section for the deep-inelastic scattering of positrons o protons at HERA is presented at momentum transfers 8:5 Q 2 35 GeV 2 and large inelasticity y = 0:7, i.e. for the Bjorken-x range 0:00013 x 0:00055. Using a next-to-leading order QCD t to the structure function F 2 at lower y values, the contribution of F 2 to the measured cross section at high y is calculated and, by subtraction, the longitudinal structure function F L is determined for the rst time with an average value of F L = 0:52 0:03 (stat) +0:25 0:22 (syst) at Q 2 = 1 5 : 4 GeV 2 and x = 0 : 000243.

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References

2010

Elementary Particle Physics

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A high statistics measurement of the proton structure functions F2(x, Q2) and R from deep inelastic muon scattering at high Q2

Cited by 505 publications

References 118 publications

Neural network determination of parton distributions: the nonsinglet case

Neural network determination of parton distributions: the nonsinglet case

Determination of the longitudinal proton structure function FL(x,Q2) at low x

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