ar X iv : h ep - p h / 05 05 04 4 v 1 6 M ay 2 00 5 The neural network approach to parton fitting

ar X iv : h ep - p h / 05 05 04 4 v 2 1 2 M ay 2 00 5 The neural network approach to parton fitting

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.

ar X iv : h ep - p h / 04 05 03 9 v 1 5 M ay 2 00 4 η , η ′ → π + π − γ with coupled channels

ar X iv : h ep - p h / 04 05 11 9 v 1 1 3 M ay 2 00 4 The Lorentz and CPT violating effects on the charged

ar X iv : h ep - p h / 04 05 03 5 v 1 5 M ay 2 00 4 Determination of the mass of the ΛΛ dibaryon by the method of QCD sum rules

ar X iv : h ep - p h / 05 09 06 7 v 1 7 S ep 2 00 5 Neural network approach to parton distributions fitting

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 nonsinglet parton distribution will be shown.