Related papers: Deconvoluting GPD from an explicit DVCS amplitude

In medical image segmentation, particularly in UNet-like architectures, upsampling is primarily used to transform smaller feature maps into larger ones, enabling feature fusion between encoder and decoder features and supporting multi-scale…

A deformed differential calculus is developed based on an associative star-product. In two dimensions the Hamiltonian vector fields model the algebra of pseudo-differential operator, as used in the theory of integrable systems. Thus one…

An analysis of Deeply Virtual Compton Scattering (DVCS) is made within the colour dipole model. We compare and contrast two models for the dipole cross-section which have been successful in describing structure function data. Both models…

We analyze sparse frame based regularization of inverse problems by means of a diagonal frame decomposition (DFD) for the forward operator, which generalizes the SVD. The DFD allows to define a non-iterative (direct) operator-adapted frame…

We consider Semi-Exclusive Deeply Virtual Compton Scattering, gamma* p \to gamma Y (SECS), where Y is an inclusive state of intermediate mass, \Lambda_QCD << m_Y << Q. When the photon is produced with a large transverse momentum k_T \sim…

We propose a new hypermatrix singular value decomposition based upon the spectral decomposition of the symmetric products of transposes.

A major part of the future COMPASS program is dedicated to the investigation of the nucleon structure through Deeply Virtual Compton Scattering (DVCS) and Deeply Virtual Meson Production (DVMP). COMPASS will measure DVCS and DVMP reactions…

A factorized Regge-pole model for deeply virtual Compton scattering is suggested. The use of an effective logarithmic Regge-Pomeron trajectory provides for the description of both ``soft'' (small $|t|$) and ``hard'' (large $|t|$) dynamics.…

We propose a novel robust decentralized graph clustering algorithm that is provably equivalent to the popular spectral clustering approach. Our proposed method uses the existing wave equation clustering algorithm that is based on…

Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extended DMD to the context of low-rank approximations. This…

Results from a recent analysis of the zero-skewness generalized parton distributions (GPDs) for valence quarks are discussed. The analysis bases on a physically motivated parameterization of the GPDs with a few free parameters adjusted to…

Distributions measured in high energy physics experiments are usually distorted and/or transformed by various detector effects. A regularization method for unfolding these distributions is re-formulated in terms of the Singular Value…

Parton distributions in impact parameter space, which are obtained by Fourier transforming GPDs, exhibit a significant deviation from axial symmetry when the target and/or quark are transversely polarized. Connections between this…

This thesis work summarizes studies in QCD for applications both at hadron colliders and in exclusive processes. In the first part we focus our attention on the study of the initial state scaling violations and the evolution of the…

We develop a new technique and calculate conformal (Gegenbauer) moments of the two-loop coefficient functions in Deeply Virtual Compton Scattering (DVCS). These results are necessary for the extraction of the generalized parton…

The photon is explored as an object to test the applications of QCD to the perturbatively calculable collinear parton distributions. We investigate analytic properties of DVCS amplitudes and related sum rules of generalized parton…

In this paper, we incorporate a graph filter deconvolution step into the classical geometric convolutional neural network pipeline. More precisely, under the assumption that the graph domain plays a role in the generation of the observed…

Singular Value Decomposition (SVD) is a powerful tool in linear algebra.We propose an extension of SVD for both the qualitative detection and quantitative determination of nonlinearity in a time series. The paper illustrates nonlinear SVD…

Generalized Parton Distributions (GPDs) offer a new way to access the quark and gluon nucleon structure. We advocate the need to supplement the experimental study of deeply virtual Compton scattering by its crossed version, timelike Compton…

Deep feature spaces have the capacity to encode complex transformations of their input data. However, understanding the relative feature-space relationship between two transformed encoded images is difficult. For instance, what is the…