About

Simonetta Liuti is a Research Professor at the University of Virginia's Department of Physics. Her research focus is not explicitly detailed on the provided page, but her title indicates a senior research role within the department. She is involved in the academic community at UVa Physics, contributing to the department's research activities and collaborations.

Research topics

• Particle physics
• Physics
• Nuclear physics
• Computer Science
• Optics
• Systems engineering
• Engineering
• Theoretical physics
• Astronomy
• Chemistry

Selected publications

• Transverse orbital angular momentum in the proton

  Physics Letters B · 2026-03-19

  articleOpen accessSenior authorCorresponding

  Using the equations of motion of QCD and Lorentz invariance relations, we show a new, more direct way of obtaining a sum rule for the proton’s quark transverse angular momentum in terms of contributions identified as spin and orbital components. The new sum rule can be understood as a twist-three relation, with the parton distribution g T encoding the quark transverse spin. We find that the complementary orbital component can be expressed in terms of a moment in the quark transverse momentum, k T , of the generalized transverse momentum-dependent distribution F 12 , or alternatively in terms of the twist-three generalized parton distributions H 2 T and E ˜ 2 T .

  PublisherDOI

• Neural Network Generalized Parton Distributions (NNGPD)

  arXiv (Cornell University) · 2026-05-13

  preprintOpen accessSenior author

  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).

  PublisherDOI

• Neural Network Representation of Generalized Parton Distributions (NNGPD)

  ArXiv.org · 2026-05-07

  articleOpen access

  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.

  PublisherOA PDF

• Neural Network Representation of Generalized Parton Distributions (NNGPD)

  arXiv (Cornell University) · 2026-05-07

  preprintOpen access

  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.

  PublisherDOI

• Neural Network Generalized Parton Distributions (NNGPD)

  ArXiv.org · 2026-05-13

  articleOpen accessSenior author

  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).

  PublisherOA PDF

• Markov chain Monte Carlo (MCMC) based Likelihood Extraction of Chiral-Odd Compton Form Factors from Deeply Virtual Exclusive Experiments

  arXiv (Cornell University) · 2026-05-18

  preprintOpen accessSenior author

  A likelihood analysis of the observables in deeply virtual exclusive meson production off a proton target is presented. 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 as well as longitudinally polarized targets from Jefferson Lab. We employ a method which derives a joint likelihood of the Compton form factors, which parameterize the deeply virtual Compton scattering amplitude in QCD, for each observed combination of the kinematic variables defining the reaction. The twist-two cross-section likelihood constrain only three of the Compton form factors (CFFs). The joint likelihood analysis of cross-section and Asymmetry information adds more sophistication to the Compton form factors (CFFs). The derived likelihoods are explored using Markov chain Monte Carlo (MCMC) methods.

  PublisherDOI

• Markov chain Monte Carlo (MCMC) based Likelihood Extraction of Chiral-Odd Compton Form Factors from Deeply Virtual Exclusive Experiments

  ArXiv.org · 2026-05-18

  articleOpen accessSenior author

  A likelihood analysis of the observables in deeply virtual exclusive meson production off a proton target is presented. 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 as well as longitudinally polarized targets from Jefferson Lab. We employ a method which derives a joint likelihood of the Compton form factors, which parameterize the deeply virtual Compton scattering amplitude in QCD, for each observed combination of the kinematic variables defining the reaction. The twist-two cross-section likelihood constrain only three of the Compton form factors (CFFs). The joint likelihood analysis of cross-section and Asymmetry information adds more sophistication to the Compton form factors (CFFs). The derived likelihoods are explored using Markov chain Monte Carlo (MCMC) methods.

  PublisherOA PDF

• VAIM-CFF: a variational autoencoder inverse mapper solution to Compton form factor extraction from deeply virtual exclusive reactions

  The European Physical Journal C · 2025-05-07 · 8 citations

  articleOpen accessSenior author

  Abstract We develop a new methodology for extracting Compton form factors (CFFs) 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.

  PublisherOA PDFDOI

• Two-body densities as a framework for dynamical imaging and their connection to ultra-peripheral collisions

  Proceedings of the International Workshop on the Physics of Ultra Peripheral Collisions · 2025-03-11

  articleOpen accessSenior author

  We present results on two-parton densities in coordinate space which capture a fuller dynamical picture of the proton’s internal structure, including information on the relative position between quarks and gluons in the transverse plane. The connection of such two-body densities to observables, proceeds in QCD, via the definition of double generalized parton distributions (DGPDs) that can be accessed in the production of two vector mesons, or two dimuon systems in ultraperipheral collisions (UPCs) through a double scattering process.

  PublisherOA PDFDOI

• Connected and disconnected contributions to nucleon form factors and parton distributions

  arXiv (Cornell University) · 2025-12-24

  preprintOpen accessSenior author

  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.

  PublisherDOI

Frequent coauthors

• Xiaochao Zheng

  University of Virginia

  247 shared

• Donal B. Day

  240 shared

• Yelena Prok

  236 shared

• Donald G. Crabb

  McCormick (United States)

  232 shared

• Matt Poelker

  Thomas Jefferson National Accelerator Facility

  225 shared

• Gary R. Goldstein

  85 shared

• H. Honkanen

  64 shared

• Saeed Ahmad

  Indian Institute of Technology Bombay

  59 shared

Education

• Ph.D.

  Rome, 'La Sapienza'

  1989

• Other

  Università degli Studi di Perugia

  1984

Awards & honors

• Fellowship in the American Physical Society
• 2019 Francis Slack Prize from SESAPS
• SESAPS Slack Award