Extraction of Information from Polarized Deep Exclusive Scattering with Machine Learning

TL;DR

This paper introduces a machine learning framework that incorporates physics constraints and lattice QCD predictions to analyze polarized deep exclusive scattering, enabling more accurate extraction of Compton Form Factors with uncertainty quantification.

Contribution

It presents a novel ML-based approach with physics-driven benchmarks for analyzing polarized scattering data, integrating symmetry constraints and lattice QCD insights.

Findings

Effective extraction of CFFs using explainable ML techniques

Incorporation of physics constraints improves analysis accuracy

Quantification of uncertainties enhances result reliability

Abstract

A framework defining benchmarks for the analysis of polarized exclusive scattering cross sections is proposed that uses physics symmetry constraints as well as lattice QCD predictions. These constraints are built into machine learning (ML) algorithms. Both physics driven and ML based benchmarks are applied to a wide range of deeply virtual exclusive processes through explainable ML techniques with controllable uncertainties. The observables, namely the Compton Form Factors (CFFs) which are convolutions of Generalized Parton Distributions (GPDs), are extracted using methods such as the random targets method to evaluate the separate contribution of the aleatoric and epistemic uncertainties in exclusive scattering analyses.