The Bjorken sum rule with Monte Carlo and Neural Network techniques

Abstract

Determinations of structure functions and parton distribution functions have been recently obtained using Monte Carlo methods and neural networks as universal, unbiased interpolants for the unknown functional dependence. In this work the same methods are applied to obtain a parametrization of polarized Deep Inelastic Scattering (DIS) structure functions. The Monte Carlo approach provides a bias--free determination of the probability measure in the space of structure functions, while retaining all the information on experimental errors and correlations. In particular the error on the data is propagated into an error on the structure functions that has a clear statistical meaning. We present the application of this method to the parametrization from polarized DIS data of the photon asymmetries $A_1^p$ and $A_1^d$ from which we determine the structure functions $g_1^p(x,Q^2)$ and $g_1^d(x,Q^2)$, and discuss the possibility to extract physical parameters from these parametrizations. This work can be used as a starting point for the determination of polarized parton distributions.

24 pages, 6 figures

References in corpus (6)

• Extraction of Spin-Dependent Parton Densities and Their Uncertainties
• Spin Structure of the Nucleon - Status and Recent Results
• Precision determination of electroweak parameters and the strange content of the proton from neutrino deep-inelastic scattering
• Experimental study of isovector spin sum rules
• Target mass corrections for spin-dependent structure functions in collinear factorization
• Extraction of characteristic constants in QCD with perturbative and nonperturbative methods

Cited by in corpus (4)

• QCD Analysis of the Polarized World Data
• QCD Analysis of Polarized Deep Inelastic Scattering Data
• Deviations from NLO QCD evolution in inclusive HERA data
• Non-Singlet spin structure function in the valon model and low x scaling behavior of $g_1^{NS}$ and $g_1^p$