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Zaki Panjsheeri

Publications and source records attributed to Zaki Panjsheeri.

8 recordsLinked to original sources
arXiv2026

Generalized Parton Distributions: Phenomenology, Extraction, and Hadron Imaging

Generalized Parton Distributions (GPDs) provide a framework for investigating the correlated momentum and spatial structure of quarks and gluons in hadrons and for accessing fundamental properties such as angular momentum and the QCD energy-momentum tensor. In this review, we discuss the present status of GPD phenomenology, emphasizing the challenges involved in connecting deeply virtual exclusive measurements to the underlying partonic structure. We organize this problem in terms of two successive inverse problems: the extraction of Compton Form Factors (CFFs) from measured observables and the reconstruction of GPDs from the convolution integrals defining the CFFs. We review the theoretical description and phenomenological parametrizations of GPDs, current strategies for CFF and GPD extraction, and the role of lattice QCD, Bayesian inference, uncertainty quantification, and artificial intelligence, including neural networks and interpretable machine-learning approaches. We discuss the limitations of present determinations and the opportunities offered by the Jefferson Lab program, complementary exclusive processes, and the future Electron-Ion Collider. Finally, we consider how increasingly precise and multidimensional information, together with new statistical and AI methodologies, will transform GPD phenomenology in the emerging era of precision hadron imaging.

hep-ph↗
arXiv2026

Neural Network Generalized Parton Distributions (NNGPD)

Generalized parton distributions (GPDs) serve as indispensable tools for the exploration of proton structure. In this study, we offer a deep learning-assisted framework for the extraction of GPDs from experimental data and the results of ab-initio lattice quantum chromodynamics (LQCD).

hep-ph↗
arXiv2026

Neural Network Representation of Generalized Parton Distributions (NNGPD)

We present a neural-network-based framework for modeling generalized parton distributions, referred to as NNGPD, in which GPDs are represented as flexible functions constrained through physically motivated integral relations. In this approach, experimental and theoretical information is incorporated into the training procedure via loss functions enforcing convolution integrals that define Compton form factors, as well as Mellin moments related to generalized form factors accessible in lattice QCD. This formulation reflects the inverse-problem character of GPD phenomenology without assuming a specific functional ansatz. As a proof of concept, we benchmark the NNGPD framework using a phenomenological spectator-based GPD model, from which synthetic training data for Compton form factors and Mellin moments are generated. The neural network is trained solely on these aggregate observables, and the resulting GPDs are compared directly with the underlying model distributions in a closure-type test. We find that the neural-network representation reproduces the main features of the GPDs over the relevant kinematic domain, despite being constrained only by their integral projections. This study demonstrates the viability of neural-network representations of GPDs constrained by global physical observables and provides a basis for future phenomenological applications combining experimental measurements of deeply virtual Compton scattering, including those anticipated at the Electron Ion Collider, with lattice QCD inputs for Mellin moments and generalized form factors.

hep-ph↗
arXiv2025

Connected and disconnected contributions to nucleon form factors and parton distributions

Using the framework of generalized parton distribution, we provide a unified interpretation of the connected and disconnected contributions from the ab-initio Euclidean path-integral formulation of the hadronic tensor in both the nucleon elastic form factors and the parton distribution functions. We develop a phenomenology to elucidate non-perturbative contributions to deep inelastic structure functions, which can be extended to observables in heavy-ion collisions probing baryon junctions.

hep-ph↗
arXiv2025

Updated flexible global parametrization of generalized parton distributions from elastic and deep inelastic inclusive scattering data

An updated flexible parametrization of the generalized parton distributions in the quark, antiquark and gluon sectors is presented using constraints from high precision electron nucleon deep inelastic scattering data, as well as from the $u$, $d$ quark and gluonic contributions to the nucleon electromagnetic elastic form factors. The latter include recently updated lattice QCD moment calculations. The generalized parton distributions in the vector sector are $H$ and $E$. We rigorously constrain the partonic components, $H_{u_v}$, $H_{d_v}$, $H_{\bar{u}}$, $H_{\bar{d}}$, $H_{\bar{s}}$ and $H_{g}$, and the analogous quantities for $E$, with proper uncertainty quantification. These distributions obey leading order perturbative QCD evolution equations in $α_S$. Parametric forms at the initial scale, $Q_o^2 = 0.58$ $\mathrm{GeV}^2$, for both quarks and gluon distributions are presented as a function of the relevant kinematic variables, namely, the parton momentum fraction, $x$, the skewness, $ξ$, and the invariant, $t$. We also present the Compton form factors entering the deeply virtual Compton scattering process in the kinematic regimes for both fixed target and electron-ion collider settings.

hep-ph↗
arXiv2025

Generalized Parton Distributions from Symbolic Regression

AI/ML informed Symbolic Regression is the next stage of scientific modeling. We utilize a highly customizable symbolic regression package ``PySR" to model the $x$ and $t$ dependence of the flavor isovector combination $H_{u-d}(x,t,ξ)$ at $ξ=0$. These PySR models were trained on GPD results provided by both Lattice QCD and phenomenological sources GGL, GK, and VGG. We demonstrate, for the first time, the consistency and systematic convergence of Symbolic Regression by quantifying the disparate models through their Taylor expansion coefficients. In addition to PySR penalizing models with higher complexity and mean-squared error, we implement schemes that test specific physics hypotheses, including force-factorized $x$ and $t$ dependence and Regge behavior in PySR GPDs. We show that PySR can identify factorizing GPD sources based on their response to the Force-Factorized model. Knowing the precise behavior of the GPDs, and their uncertainties in a wide range in $x$ and $t$, crucially impacts our ability to concretely and quantitatively predict hadronic spatial distributions and their derived quantities.

hep-ph↗
arXiv2025

Likelihood and Correlation Analysis of Compton Form Factors for Deeply Virtual Exclusive Scattering on the Nucleon

A likelihood analysis of the observables in deeply virtual exclusive photoproduction off a proton target, $ep \rightarrow e' p' γ'$, is presented. Two processes contribute to the reaction: deeply virtual Compton scattering, where the photon is produced at the proton vertex, and the Bether-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.

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