Variational autoencoder inverse mapper for extraction of Compton form factors: Benchmarks and conditional learning

Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theore...

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Bibliographic Details
Published inarXiv.org
Main Authors Hossen, Fayaz, Adams, Douglas, Bautista, Joshua, Li, Yaohang, Gia-Wei Chern, Liuti, Simonetta, Boer, Marie, Cuic, Marija, Goldstein, Gari R, Engelhardt, Michael, Huey-Wen, Li
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 21.08.2024
Subjects
Constraints
Elastic scattering
Form factors
Fourier transforms
Kinematics
Markov chains
Momentum transfer
Partons
Quantum chromodynamics
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Summary:Deeply virtual exclusive scattering processes (DVES) serve as precise probes of nucleon quark and gluon distributions in coordinate space. These distributions are derived from generalized parton distributions (GPDs) via Fourier transform relative to proton momentum transfer. QCD factorization theorems enable DVES to be parameterized by Compton form factors (CFFs), which are convolutions of GPDs with perturbatively calculable kernels. Accurate extraction of CFFs from DVCS, benefiting from interference with the Bethe-Heitler (BH) process and a simpler final state structure, is essential for inferring GPDs. This paper focuses on extracting CFFs from DVCS data using a variational autoencoder inverse mapper (VAIM) and its constrained variant (C-VAIM). VAIM is shown to be consistent with Markov Chain Monte Carlo (MCMC) methods in extracting multiple CFF solutions for given kinematics, while C-VAIM effectively captures correlations among CFFs across different kinematic values, providing more constrained solutions. This study represents a crucial first step towards a comprehensive analysis pipeline towards the extraction of GPDs.
ISSN:2331-8422