5 papers
Differentiable Principal-Value Inversion for Neural-Network Extraction of Generalized Parton Distributions
Dima Watkins, Ishara Fernando, Dustin Keller
We present a machine-learning method for the nonparametric extraction of generalized parton distributions (GPDs) from Compton form factors (CFFs) constrained by experimental data.…
Scheme-invariant stratified factorization algebras for inclusive deep inelastic scattering
Dustin Keller
Inclusive deep inelastic scattering factorization combines two features that are often treated separately: an asymptotic reconstruction of the current-current matrix element from h…
A Core Representation Theorem for Scheme-Invariant Collinear Factorization in QCD
Dustin Keller
Collinear factorization and the leading-twist operator product expansion (OPE) in perturbative QCD express suitably inclusive observables in scale-separated kinematics as composite…
Toward selective quantum advantage in hadronic tomography:explicit cases from Compton form factors, GPDs, TMDs, and GTMDs
I. P. Fernando, D. Keller
We recast the case for quantum advantage in hadronic physics as an observable-by-observable question rather than a blanket claim about Quantum Chromo-Dynamics (QCD). Focusing on ha…
Experimental Uncertainty Propagation in Neural Network Extraction in Hadronic Physics
Dustin Keller
Obtaining Compton Form Factors (CFFs) and Transverse Momentum Dependent parton distribution functions (TMDs) from experimental data using neural network-based information extractio…