We will show an application of neural networks to extract informations 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.
Neural network approach to parton distributions fitting / A. Piccione, L. Del Debbio, S. Forte, J.I. Latorre, J. Rojo. - In: NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION A, ACCELERATORS, SPECTROMETERS, DETECTORS AND ASSOCIATED EQUIPMENT. - ISSN 0168-9002. - 559:1(2006 Apr 01), pp. 203-206. [10.1016/j.nima.2005.11.206]
Neural network approach to parton distributions fitting
S. Forte;
2006
Abstract
We will show an application of neural networks to extract informations 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.Pubblicazioni consigliate
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