Document Type

Article

Publication Date

2026

DOI

10.1515/mcma-2026-3007

Publication Title

Monte Carlo Methods and Applications

Volume

32

Issue

2

Pages

205-219

Abstract

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.

Rights

© 2026 the authors.

This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Adams, D., Hossen, M. D. F. B., Bautista, J., Chern, G.-W., Liuti, S., Boër, M., Čuić, M., Engelhardt, M., Goldstein, G. R., Lin, H.-W., & Li, Y. (2026). Variational autoencoder inverse mapper for extraction of Compton form factors: Benchmarks and conditional learning. Monte Carlo Methods and Applications, 32(2), 205-219. https://doi.org/10.1515/mcma-2026-3007

ORCID

0009-0008-5109-2466 (Hossen), 0000-0003-0178-1876 (Li)

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