Self-Organizing Maps and Parton Distributions Functions
High Energy Physics - Phenomenology
2017-08-23 v1
Abstract
We present a new method to extract parton distribution functions from high energy experimental data based on a specific type of neural networks, the Self-Organizing Maps. We illustrate the features of our new procedure that are particularly useful for an anaysis directed at extracting generalized parton distributions from data. We show quantitative results of our initial analysis of the parton distribution functions from inclusive deep inelastic scattering.
Cite
@article{arxiv.1008.4197,
title = {Self-Organizing Maps and Parton Distributions Functions},
author = {K. Holcomb and S. Liuti and D. Z. Perry},
journal= {arXiv preprint arXiv:1008.4197},
year = {2017}
}
Comments
8 pages, 4 figures, to appear in the proceedings of "Workshop on Exclusive Reactions at High Momentum Transfer (IV)", Jefferson Lab, May 18th -21st, 2010