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• W1998937259 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 A1p and A1d from which we determine the structure functions g1p(x,Q2) and g1d(x,Q2), 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." @default.
• W1998937259 created "2016-06-24" @default.
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• W1998937259 date "2009-11-12" @default.
• W1998937259 modified "2023-09-26" @default.
• W1998937259 title "The Bjorken sum rule with Monte Carlo and Neural Network techniques" @default.
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• W1998937259 doi "https://doi.org/10.1088/1126-6708/2009/11/060" @default.
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