Neural network approach to parton distributions fitting
High Energy Physics - Phenomenology
2019-08-14 v2
Abstract
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.
Cite
@article{arxiv.hep-ph/0509067,
title = {Neural network approach to parton distributions fitting},
author = {Andrea Piccione and Joan Rojo},
journal= {arXiv preprint arXiv:hep-ph/0509067},
year = {2019}
}
Comments
4 pages, 5 eps figures. Talk given by Andrea Piccione at the "X International Workshop on Advanced Computing and Analysis Techniques in Physics Research", ACAT 2005, DESY-Zeuthen, Germany, 22-27 May 2005. Corrected fig. 4