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arxiv: 2408.11681 · v1 · pith:GHWH4QQC · submitted 2024-08-21 · hep-ph

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

Fayaz Hossen , Douglas Adams , Joshua Bautista , Yaohang Li , Gia-Wei Chern , Simonetta Liuti , Marie Boer , Marija Cuic
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Gari R. Goldstein Michael Engelhardt Huey-Wen Li
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classification hep-ph
keywords cffsgpdsdistributionsextractionautoencoderc-vaimcomptonconstrained
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Constraining DVCS Compton Form Factors Using Lattice QCD informed Neural Network

    hep-ph 2026-06 unverdicted novelty 5.0

    A neural network framework informed by lattice QCD uses all-order dispersion relations to significantly constrain both real and imaginary parts of Compton Form Factors extracted from DVCS proton data.

  2. Neural Network Representation of Generalized Parton Distributions (NNGPD)

    hep-ph 2026-05 unverdicted novelty 5.0

    A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.