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We construct a parametrization of the deep-inelastic structure function of the proton F2(x,Q ) based on all available experimental information from charged lepton deep-inelastic scattering experiments. The parametrization effectively provides a bias-free determination of the probability measure in the space of structure functions, which retains information(More)
We find a new family of exact solutions in membrane theory, representing toroidal membranes spinning in several planes. They have energy square proportional to the sum of the different angular momenta, generalizing Regge-type string solutions to membrane theory. By compactifying the eleven dimensional theory on a circle and on a torus, we identify a family(More)
We show that the well-known divergence of the perturbative expansion of resummed results for processes such as deep-inelastic scattering and Drell-Yan in the soft limit can be treated by Borel resummation. The divergence in the Borel inversion can be removed by the inclusion of suitable higher twist terms. This provides us with an alternative to the(More)
We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.
We study QCD in 1+1 dimensions in the large Nc limit using light-front Hamiltonian perturbation theory in the 1/Nc expansion. We use this formalism to exactly compute hadronic transition matrix elements for arbitrary currents at leading order in 1/Nc. We compute the semileptonic differential decay rate of a heavy meson, dΓ/dx, and its moments, MN , using(More)
We study QCD in 1+1 dimensions in the large Nc limit using light-front Hamiltonian perturbation theory in the 1/Nc expansion. We use this formalism to exactly compute hadronic transition matrix elements for arbitrary currents at leading order in 1/Nc. We compute the semileptonic differential decay rate of a heavy meson, dΓ/dx, and its moments, MN , using(More)
We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.
We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the(More)