The extraction of Compton form factors (CFFs) in a global analysis of almost all deeply virtual Compton scattering (DVCS) proton data is presented. The extracted quantities are DVCS sub-amplitudes ...and the most basic observables which are unambiguously accessible from this process. The parameterizations of CFFs are constructed utilizing the artificial neural network technique allowing for an important reduction of model dependency. The analysis consists of such elements as feasibility studies, training of neural networks with the genetic algorithm and a careful regularization to avoid over-fitting. The propagation of experimental uncertainties to extracted quantities is done with the replica method. The resulting parameterizations of CFFs are used to determine the subtraction constant through dispersion relations. The analysis is done within the PARTONS framework.
We propose new parameterizations for the border and skewness functions appearing in the description of 3D nucleon structure in the language of generalized parton distributions (GPDs). These ...parameterizations are constructed in a way to fulfill the basic properties of GPDs, like their reduction to parton density functions and elastic form factors. They also rely on the power behavior of GPDs in the
x
→
1
limit and the propounded analyticity property of Mellin moments of GPDs. We evaluate compton form factors (CFFs), the sub-amplitudes of the deeply virtual compton scattering (DVCS) process, at the leading order and leading twist accuracy. We constrain the restricted number of free parameters of these new parameterizations in a global CFF analysis of almost all existing proton DVCS measurements. The fit is performed within the PARTONS framework, being the modern tool for generic GPD studies. A distinctive feature of this CFF fit is the careful propagation of uncertainties based on the replica method. The fit results genuinely permit nucleon tomography and may give some insight into the distribution of forces acting on partons.
We discuss the use of machine learning techniques in effectively nonparametric modelling of generalised parton distributions (GPDs) in view of their future extraction from experimental data. Current ...parameterisations of GPDs suffer from model dependency that lessens their impact on phenomenology and brings unknown systematics to the estimation of quantities like Mellin moments. The new strategy presented in this study allows to describe GPDs in a way fulfilling theory-driven constraints, keeping model dependency to a minimum. Getting a better grip on the control of systematic effects, our work will help the GPD phenomenology to achieve its maturity in the precision era commenced by the new generation of experiments.
A unique feature of generalised parton distributions is their relation to the QCD energy–momentum tensor. In particular, they provide access to the mechanical properties of the proton i.e. the ...distributions of pressure and shear stress induced by its quark and gluon structure. In principle the pressure distribution can be experimentally determined in a model-independent way from a dispersive analysis of deeply virtual Compton scattering data through the measurement of the subtraction constant. In practice the kinematic coverage and accuracy of existing experimental data make this endeavour a challenge. Elaborating on recent global fits of deeply virtual Compton scattering measurements using artificial neural networks, our analysis presents the current knowledge on this subtraction constant and assesses the impact of the most frequent systematic assumptions made in this field of research. This study will pave the way for future works when more precise data will become available, e.g. obtained in the foreseen electron-ion colliders EIC and EIcC.
Generalized parton distributions are instrumental to study both the three-dimensional structure and the energy-momentum tensor of the nucleon, and motivate numerous experimental programs involving ...hard exclusive measurements. Based on a next-to-leading order analysis and a careful study of evolution effects, we exhibit nontrivial generalized parton distributions with arbitrarily small imprints on deeply virtual Compton scattering observables. This means that in practice the reconstruction of generalized parton distributions from measurements, known as the deconvolution problem, does not possess a unique solution for this channel. In this paper we discuss the consequences on the extraction of generalized parton distributions from data and advocate for a multichannel analysis.
We present a novel approach to compute generalized parton distributions within the lightfront wave function overlap framework. We show how to systematically extend generalized parton distributions ...computed within the DGLAP region to the ERBL one, fulfilling at the same time both the polynomiality and positivity conditions. We exemplify our method using pion lightfront wave functions inspired by recent results of non-perturbative continuum techniques and algebraic nucleon lightfront wave functions. We also test the robustness of our algorithm on reggeized phenomenological parameterizations. This approach paves the way to a better understanding of the nucleon structure from non-perturbative techniques and to a unification of generalized parton distributions and transverse momentum dependent parton distribution functions phenomenology through lightfront wave functions.
Data-driven study of timelike Compton scattering Grocholski, O.; Moutarde, H.; Pire, B. ...
The European physical journal. C, Particles and fields,
02/2020, Volume:
80, Issue:
2
Journal Article
Peer reviewed
Open access
In the framework of collinear QCD factorization, the leading twist scattering amplitudes for deeply virtual Compton scattering (DVCS) and timelike Compton scattering (TCS) are intimately related ...thanks to analytic properties of leading and next-to-leading order amplitudes. We exploit this welcome feature to make data-driven predictions for TCS observables to be measured in near future experiments. Using a recent extraction of DVCS Compton form factors from most of the existing experimental data for that process, we derive TCS amplitudes and calculate TCS observables only assuming leading-twist dominance. Artificial neural network techniques are used for an essential reduction of model dependency, while a careful propagation of experimental uncertainties is achieved with replica methods. Our analysis allows for stringent tests of the leading twist dominance of DVCS and TCS amplitudes. Moreover, this study helps to understand quantitatively the complementarity of DVCS and TCS measurements to test the universality of generalized parton distributions, which is crucial
e.g.
to perform the nucleon tomography.
We systematically evaluate observables for hard exclusive electroproduction of real photons and compare them to experiment using a set of Generalized Parton Distributions (GPDs) whose parameters are ...constrained by Deeply Virtual Meson Production data, nucleon form factors and parton distributions. The Deeply Virtual Compton Scattering amplitudes are calculated to leading-twist accuracy and leading order in QCD perturbation theory while the leptonic tensor is treated exactly, without any approximation. This study constitutes a check of the universality of the GPDs. We summarize all relevant details on the parameterizations of the GPDs and describe its use in the handbag approach of the aforementioned hard scattering processes. We observe good agreement between predictions and measurements of deeply virtual Compton scattering on a wide kinematic range, including most data from H1, ZEUS, HERMES, Hall A and CLAS collaborations for unpolarized and polarized targets when available. We also give predictions relevant for future experiments at COMPASS and JLab after the 12 GeV upgrade.
In order to learn effectively from measurements of generalised parton distributions (GPDs), it is desirable to compute them using a framework that can potentially connect empirical information with ...basic features of the Standard Model. We sketch an approach to such computations, based upon a rainbow-ladder (RL) truncation of QCD's Dyson–Schwinger equations and exemplified via the pion's valence dressed-quark GPD, Hπv(x,ξ,t). Our analysis focuses primarily on ξ=0, although we also capitalise on the symmetry-preserving nature of the RL truncation by connecting Hπv(x,ξ=±1,t) with the pion's valence-quark parton distribution amplitude. We explain that the impulse-approximation used hitherto to define the pion's valence dressed-quark GPD is generally invalid owing to omission of contributions from the gluons which bind dressed-quarks into the pion. A simple correction enables us to identify a practicable improvement to the approximation for Hπv(x,0,t), expressed as the Radon transform of a single amplitude. Therewith we obtain results for Hπv(x,0,t) and the associated impact-parameter dependent distribution, qπv(x,|b→⊥|), which provide a qualitatively sound picture of the pion's dressed-quark structure at a hadronic scale. We evolve the distributions to a scale ζ=2 GeV, so as to facilitate comparisons in future with results from experiment or other nonperturbative methods.
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