Self-Organizing Maps Parametrization of Deep Inelastic Structure Functions with Error Determination
High Energy Physics - Phenomenology
2013-09-30 v1
Abstract
We present and discuss a new method to extract parton distribution functions from hard scattering processes based on an alternative type of neural network, the Self-Organizing Map. Quantitative results including a detailed treatment of uncertainties are presented within a Next to Leading Order analysis of inclusive electron proton deep inelastic scattering data.
Cite
@article{arxiv.1309.7085,
title = {Self-Organizing Maps Parametrization of Deep Inelastic Structure Functions with Error Determination},
author = {Evan Askanazi and Katherine Holcomb and Simonetta Liuti},
journal= {arXiv preprint arXiv:1309.7085},
year = {2013}
}
Comments
16 pages, 12 figures