Neural network determination of parton distributions: The nonsinglet case

Journal of High Energy Physics

Volumn 2007, Issue 3, 2007, Pages

  Del Debbio, Luigi ^a   Forte, Stefano ^b   Latorre, José I ^c   Piccione, Andrea ^d   Rojo, Joan ^c,e  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b UNIVERSITY OF MILAN   (Italy)

^c UNIVERSITY OF BARCELONA   (Spain)

^d INFN SEZIONE DI GENOVA   (Italy)

^e LPTHE   (France)

Author keywords

Deep inelastic scattering; Parton model; QCD

Indexed keywords

EID: 33947708954     PISSN: 10298479     EISSN: 10298479     Source Type: Journal    
DOI: 10.1088/1126-6708/2007/03/039     Document Type: Article

References (37)

1. S. Catani et al., QCD [hep-ph/0005025]
2. M. Dittmar et al., Parton distributions: summary report for the HERA-LHC workshop [hep-ph/0511119]
3. S. Forte, Structure functions and parton distributions 2005 Nucl. Phys. A 755 100 [hep-ph/0502073]
4. S. Alekhin, Parton distributions from deep-inelastic scattering data 2003 Phys. Rev. D 68 014002 [hep-ph/0211096]
5. J. Pumplin et al., New generation of parton distributions with uncertainties from global QCD analysis 2002 J. High Energy Phys. JHEP07(2002)012 [hep-ph/0201195]
6. A.D. Martin, R.G. Roberts, W.J. Stirling and R.S. Thorne, Uncertainties of predictions from parton distributions, I. Experimental errors 2003 Eur. Phys. J. C 28 455 [hep-ph/0211080]
7. W.T. Giele, S.A. Keller and D.A. Kosower, Parton distribution function uncertainties [hep-ph/0104052]
8. A. Djouadi and S. Ferrag, Pdf uncertainties in Higgs production at hadron colliders 2004 Phys. Lett. B 586 345 [hep-ph/0310209]
9. S. Forte, L. Garrido, J.I. Latorre and A. Piccione, Neural network parametrization of deep-inelastic structure functions 2002 J. High Energy Phys. JHEP05(2002)062 [hep-ph/0204232]
10. NNPDF collaboration, L. Del Debbio, S. Forte, J.I. Latorre, A. Piccione and J. Rojo, Unbiased determination of the proton structure function F2(P) with faithful uncertainty estimation 2005 J. High Energy Phys. JHEP03(2005)080 [hep-ph/0501067]
11. J. Rojo and J.I. Latorre, Neural network parametrization of spectral functions from hadronic tau decays and determination of QCD vacuum condensates 2004 J. High Energy Phys. JHEP01(2004)055 [hep-ph/0401047]
12. J. Rojo, Neural network parametrization of the lepton energy spectrum in semileptonic b meson decays 2006 J. High Energy Phys. JHEP05(2006)040 [hep-ph/0601229]
13. M.C. Gonzalez-Garcia, M. Maltoni and J. Rojo, Determination of the atmospheric neutrino fluxes from atmospheric neutrino data 2006 J. High Energy Phys. JHEP10(2006)075 [hep-ph/0607324]
14. G. D'Agostini, Bayesian reasoning in data analysis: a critical introduction (World Scientific), New Jersey, USA, 2003, p. 329
15. G. Cowan, Statistical data analysis Oxford Science Publications 2002
16. M. Diemoz, F. Ferroni, E. Longo and G. Martinelli, Parton densities from deep inelastic scattering to hadronic processes at super collider energies 1988 Z. Physik C 39 21
17. E665 collaboration, M.R. Adams et al., Proton and deuteron structure functions in muon scattering at 470-gev 1996 Phys. Rev. D 54 3006
18. t 1997 Nucl. Phys. B 483 3 [hep-ph/9610231]
19. 2 1989 Phys. Lett. B 223 485
20. 2 1990 Phys. Lett. B 237 592
21. J.A.M. Vermaseren, A. Vogt and S. Moch, The third-order QCD corrections to deep-inelastic scattering by photon exchange 2005 Nucl. Phys. B 724 3 [hep-ph/0504242]
22. S. Moch, J.A.M. Vermaseren and A. Vogt, The three-loop splitting functions in QCD: the non-singlet case 2004 Nucl. Phys. B 688 101 [hep-ph/0403192]
23. A. Vogt, Efficient evolution of unpolarized and polarized parton distributions with QCD-pegasus 2005 Comput. Phys. Commun. 170 65 [hep-ph/0408244]
24. M. Botje, Qcdnum manual http://www.nikhef.nl/∼h24/qcdnum/
25. G. P. Salam, Hoppet manual http://hepforge.cedar.ac.uk/hoppet/
26. Particle Data Group collaboration, W. M. Yao et al., Review of particle physics 2006 J. Phys. G 33 1
27. K.G. Chetyrkin, B.A. Kniehl and M. Steinhauser, Strong coupling constant with flavour thresholds at four loops in the scheme, 1997 Phys. Rev. Lett. 79 2184 [hep-ph/9706430]
28. R.S. Thorne, A variable-flavour number scheme for NNLO 2006 Phys. Rev. D 73 054019 [hep-ph/0601245]
29. J. Abate and P. Valko, Multi-precision laplace transform inversion 2003 International Journal for Numerical Methods in Engineering 60 979
30. W. Giele et al., The QCD/SM working group: summary report [hep-ph/0204316]
31. H. Georgi and H.D. Politzer, Freedom at moderate energies: masses in color dynamics 1976 Phys. Rev. D 14 1829
32. R.G. Roberts, The Structure of the proton: Deep inelastic scattering. (Cambridge Univ. Press)., UK, 1990
33. Y. A. Kanev, Application of neural networks and genetic algorithms in high-energy physics UMI-99-05968
34. L. Lonnblad, C. Peterson and T. Rognvaldsson, Using neural networks to identify jets 1991 Nucl. Phys. B 349 675
35. 2) up to NNLO 2006 Nucl. Phys. B 754 178 [hep-ph/0604116]
36. 3) [hep-ph/0607200]
37. S. Forte and G. Ridolfi, Renormalization group approach to soft gluon resummation 2003 Nucl. Phys. B 650 229 [hep-ph/0209154]