Reweighting a parton shower using a neural network: the final-state case
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The use of QCD calculations that include the resummation of soft-collinear logarithms via parton-shower algorithms is currently not possible in PDF fits due to the high computational cost of evaluating observables for each variation of the PDFs. Unfortunately the interpolation methods that are otherwise applied to overcome this issue are not readily generalised to all-order parton-shower contributions. Instead, we propose an approximation based on training a neural network to predict the effect of varying the input parameters of a parton shower on the cross section in a given observable bin, interpolating between the variations of a training data set. This first publication focuses on providing a proof-of-principle for the method, by varying the shower dependence on αS for both a simplified shower model and a complete shower implementation for three different observables, the leading emission scale, the number of emissions and the Thrust event shape. The extension to the PDF dependence of the initial-state shower evolution that is needed for the application to PDF fits is left to a forthcoming publication.
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- T. Kluge, K. Rabbertz and M. Wobisch, FastNLO: Fast pQCD calculations for PDF fits, in proceedings of the 14th International Workshop on Deep Inelastic Scattering (DIS 2006), Tsukuba, Japan, 20–24 April 2006, World Scientific (2007), pp. 483-486 [hep-ph/0609285] [INSPIRE] and online pdf version at http://lss.fnal.gov/cgi-bin/find_paper.pl?conf-06-352.
- fastNLO collaboration, D. Britzger, K. Rabbertz, F. Stober and M. Wobisch, New features in version 2 of the fastNLO project, in proceedings of the 20th International Workshop on Deep-Inelastic Scattering and Related Subjects (DIS 2012), Bonn, Germany, 26–30 March 2012, M. Kuze, K. Nagano and K. Tokushuku, World Scientific, Hackensack U.S.A. (2007) [arXiv:1208.3641] [INSPIRE].
- E. Bothmann, N. Hartland and S. Schumann, Introducing MCgrid 2.0: Projecting cross section calculations on grids, Comput. Phys. Commun. 196 (2015) 617 [INSPIRE].
- V.N. Gribov and L.N. Lipatov, Deep inelastic e p scattering in perturbation theory, Sov. J. Nucl. Phys. 15 (1972) 438 [Yad. Fiz. 15 (1972) 781] [INSPIRE].
- Y.L. Dokshitzer, Calculation of the Structure Functions for Deep Inelastic Scattering and e + e − Annihilation by Perturbation Theory in Quantum Chromodynamics., Sov. Phys. JETP 46 (1977) 641 [Zh. Eksp. Teor. Fiz. 73 (1977) 1216] [INSPIRE].
- V.V. Sudakov, Vertex parts at very high-energies in quantum electrodynamics, Sov. Phys. JETP 3 (1956) 65 [Zh. Eksp. Teor. Fiz. 30 (1956) 87] [INSPIRE].
- A. Paszke et al., Automatic differentiation in PyTorch, in proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, U.S.A., 4–9 December 2017.Google Scholar
- ALEPH collaboration, A. Heister et al., Studies of QCD at e + e − centre-of-mass energies between 91 and 209 GeV, Eur. Phys. J. C 35 (2004) 457 [INSPIRE].