Related papers: DVCS and the skewness effect at small x
Generalized Parton Distributions (GPDs) are multidimensonal structure functions that encode the information about the internal structure of hadrons. Using privileged channels such as Deeply Virtual Compton Scattering (DVCS) or Timelike…
Generalized parton distribution (GPD) contains rich information of partons in a hadron, including transverse profile, and is also non-perturbative information necessary in describing a variety of hard processes, such as meson…
Gaussian process (GP) models that combine both categorical and continuous input variables have found use in analysis of longitudinal data and computer experiments. However, standard inference for these models has the typical cubic scaling,…
When using stochastic gradient descent to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data across multiple machines if needed, and then perform several…
McKean-Vlasov stochastic differential equations (MV-SDEs) provide a mathematical description of the behavior of an infinite number of interacting particles by imposing a dependence on the particle density. As such, we study the influence of…
Generalized Parton Distributions (GPDs) are multidimensional structure functions of hadrons, encoding mechanical and spin properties through the correlation of the momentum and transverse position of partons. While channels like Deeply…
Access to Generalised Parton Distributions (GPDs) through Deeply Virtual Compton Scattering (DVCS) is briefly described. Presently available experimental results on DVCS are summarized in conjunction with plans for future measurements.
A series of previous papers develops a dipole model in initial state impact parameter space that includes subleading effects such as running alpha strong, unitarity, confinement and saturation. Here some recent work is presented, where the…
We study Generalized Parton Distribution functions (GPDs) usually measured in hard exclusive processes and encoding information on the three dimensional partonic structure of hadrons and their spin decomposition, for non zero skewness…
Having in mind the well-known limitations of certain models of generalized parton distributions (GPDs), we show that the allegedly universal GPDs, describing both deeply virtual Compton scattering (DVCS) and deeply virtual meson production…
In this paper, we discuss the feasibility of measuring deeply virtual Compton scattering (DVCS) on nuclei in a collider setting, as for example, the planned high-luminosity Electron-Ion-Collider (EIC). We demonstrate that employing our…
We perform a global QCD analysis of the pion electromagnetic form factor (FF) data from pion electroproduction and elastic pion scattering to extract the valence pion generalized parton distributions (GPDs) at zero skewness. The analysis…
We discuss a new leading-order parameterization of generalized parton distributions of the proton, which is based on the idea of duality. In its minimal version, the parameterization is defined by the usual quark singlet parton…
We study the asymptotic properties of the small data solutions of the Vlasov-Maxwell system in dimension three. No neutral hypothesis nor compact support assumptions are made on the data. In particular, the initial decay in the velocity…
Stochastic gradient descent (SGD) is a popular algorithm for minimizing objective functions that arise in machine learning. For constant step-sized SGD, the iterates form a Markov chain on a general state space. Focusing on a class of…
We propose a reconstruction of the full $(x, \xi, t)$ dependence of unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}$ and $E^{u-d}$ from lattice QCD data in the pseudo-distribution formalism. For the first time,…
We present a new model for generalized parton distributions (GPDs), based on the aligned jet model, which successfully describes the deeply virtual Compton scattering (DVCS) data from H1, ZEUS, HERMES and CLAS. We also present an easily…
We review Shuvaev's transformations, that relate off-forward parton distributions (OFPDs) to so-called effective forward parton distributions (EFPDs). The latter evolve like conventional forward partons. We express nonforward amplitudes,…
The development of decentralized stability conditions has gained considerable attention due to the need to analyze multi-agent network systems, such as heterogeneous multi-converter power systems. A recent advance is the application of the…
The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through…