Studying Deeply Virtual Compton Scattering with Neural Networks
Kresimir Kumericki, Dieter Mueller, Andreas Schafer
TL;DR
This paper employs neural networks to analyze deeply virtual Compton scattering data, accurately fitting form factors and predicting experimental asymmetries for upcoming measurements.
Contribution
It introduces a neural network-based method to fit Compton form factors and predict experimental asymmetries in deeply virtual Compton scattering.
Findings
Successful fit of Compton form factor H to HERMES data
Predicted beam charge-spin asymmetry for COMPASS II
Demonstrated neural networks' effectiveness in scattering analysis
Abstract
Neural networks are utilized to fit Compton form factor H to HERMES data on deeply virtual Compton scattering off unpolarized protons. We used this result to predict the beam charge-spin assymetry for muon scattering off proton at the kinematics of the COMPASS II experiment.
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Particle Detector Development and Performance · Neutrino Physics Research