Research
Understanding the innermost structure of visible matter in the universe is one of the central goals of contemporary nuclear physics, as articulated in the NSAC 2015 Long Range Plan for Nuclear Science, as well as in the multiple NP community meetings and White Papers preceding the upcoming new plan. At the most fundamental level of description, the atomic nucleus and its constituents, the nucleons, emerge as complex, relativistic many-body systems, the internal imaging of which presents a central challenge of Quantum Chromodynamics (QCD) and modern nuclear science. Mirroring the consensus of the US nuclear science community, the DOE Nuclear Physics program has committed vast resources to experimental facilities - foremost the upgraded 12 GeV Jefferson Lab accelerator and the planned Electron-Ion Collider (EIC) - to advance the tomography of nucleons. The new generation of DOE experiments will allow us to image, measure, and explore the deepest parts of the quantum world.
For more information, please contact us at 3dhadron@virginia.edu.
VAIM-CFF: A variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions
Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuiti
Abstract
We develop a new methodology for extracting Compton form factors (CFFs) in from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted CFFs, use of uncertainty quantification technique, and inclusion of prior physics information. We then use dimensionality reduction techniques such as principal component analysis to visualize the missing physics information tracked in the latent space of the VAIM framework. Through re-framing the extraction of CFFs as an inverse problem, we gain access to fundamental properties of the problem not comprehensible in standard fitting methodologies: exploring the limits of the information encoded in deeply virtual exclusive experiments.
Publications & Talks
PublicationVariational 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, Gari R. Goldstein, Michael Engelhardt, Huey-Wen Li
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.
Publications & Talks
PublicationLikelihood and Correlation Analysis of Compton Form Factors for Deeply Virtual Exclusive Scattering on the Nucleon
Douglas Q. Adams, Joshua Bautista, Marija Cuic, Adil Khawaja, Saraswati Pandey, Zaki Panjsheeri, Gia-Wei Chern, Yaohang Li, Simonetta Liuti, Marie Boer, Michael Engelhardt, Gary R. Goldstein, Huey-Wen Lin, Matthew D. Sievert
Abstract
A likelihood analysis of the observables in deeply virtual exclusive photoproduction off a proton target, , is presented. Two processes contribute to the reaction: deeply virtual Compton scattering, where the photon is produced at the proton vertex, and the Bethe-Heitler process, where the photon is radiated from the electron. We consider the unpolarized process for which the largest amount of data with all the kinematic dependences are available from corresponding datasets with unpolarized beams and unpolarized targets from Jefferson Lab. We provide and use a method which derives a joint likelihood of the Compton form factors, which parametrize the deeply virtual Compton scattering amplitude in QCD, for each observed combination of the kinematic variables defining the reaction. The unpolarized twist-two cross section likelihood fully constrains only three of the Compton form factors (CFFs). The impact of the twist-three corrections to the analysis is also explored. The derived likelihoods are explored using Markov chain Monte Carlo (MCMC) methods. Using our proposed method we derive CFF error bars and covariances. Additionally, we explore methods which may reduce the magnitude of error bars/contours in the future.