Self-Organizing Maps Parametrization of Deep Inelastic Structure Functions with Error Determination

TL;DR

This paper introduces a novel neural network approach using Self-Organizing Maps to extract parton distribution functions from deep inelastic scattering data, with detailed uncertainty analysis in a Next-to-Leading Order framework.

Contribution

It presents a new method employing Self-Organizing Maps for parton distribution function extraction, including comprehensive error determination.

Findings

Successful application to electron-proton scattering data

Quantitative uncertainty estimates provided

Compatible with Next-to-Leading Order QCD analysis

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.