The Bjorken sum rule with Monte Carlo and Neural Network techniques

Details The Bjorken sum rule with Monte Carlo and Neural Network techniques The Bjorken sum rule with Monte Carlo and Neural Network techniques

2009-07-15

arxiv.org/abs/0907.2506v1

JHEP 0911:060,2009

Physics

High Energy Physics

High Energy Physics - Phenomenology

24 pages, 6 figures

Scientific paper

10.1088/1126-6708/2009/11/060

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.

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