Monte Carlo analysis of CLAS data

TL;DR

This paper introduces a Monte Carlo-based neural network method to analyze polarized deep inelastic scattering data, specifically applied to CLAS measurements on a polarized proton target.

Contribution

It combines Monte Carlo techniques with neural network parametrization for analyzing scattering asymmetries, offering a novel approach for data fitting in particle physics.

Findings

Successful fit of virtual-photon scattering asymmetry data

Application to CLAS polarized proton data demonstrates method effectiveness

Provides a flexible, redundant parametrization for complex data analysis

Abstract

We present a fit of the virtual-photon scattering asymmetry of polarized Deep Inelastic Scattering which combines a Monte Carlo technique with the use of a redundant parametrization based on Neural Networks. We apply the result to the analysis of CLAS data on a polarized proton target.