Related papers: A method to rescale experimental data with depende…
We show the role played by QCD evolution and skewedness effects in the DVCS cross section at large $Q^2$ within the color dipole description of the process at photon level. The dipole cross section is given by the saturation model, which…
The scaling in $\sigma_{\gamma^*p}(W^2, Q^2)$ cross sections (for $Q^2/W^2 << 1$) in terms of the scaling variable $\eta = (Q^2 + m^2_0)/\Lambda^2 (W^2)$ is interpreted in the generalized vector dominance/color-dipole picture (GVD/CDP). The…
The Generalized Parton Distributions (GPDs) constitute an appropriate framework for a universal description of the partonic structure of the nucleon. Double Deeply Virtual Compton Scattering (DDVCS) process provides the only experimental…
Photon-counting computed tomography (PCCT) has emerged as a promising imaging technique, enabling spectral imaging and material decomposition (MD). However, images typically suffer from a low signal-to-noise ratio (SNR) due to constraints…
We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration.…
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…
We have developped a fitting code based on the leading-twist handbag Deep Virtual Compton Scattering (DVCS) amplitude in order to extract the Generalized Parton Distributions (GPD) information from DVCS observables in the valence region. In…
In this work we estimate the differential cross section for the high energy deeply virtual Compton scattering on a photon target within the QCD dipole-dipole scattering formalism. For the phenomenology, a saturation model for the…
The variational discrete element method developed in [28] for dynamic elasto-plastic computations is adapted to compute the deformation of elastic Cosserat materials. In addition to cellwise displacement degrees of freedom (dofs), cellwise…
The generalized parton distributions (GPDs) provide a new description of the complex internal structure of the nucleon in terms of its elementary constituents, the quarks and the gluons. The GPDs describe the correlation between the…
We present a method of measuring galaxy power spectrum based on the multiresolution analysis of the discrete wavelet transformation (DWT). Since the DWT representation has strong capability of suppressing the off-diagonal components of the…
DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering data points based on density distribution and (2) identifying…
Study of the structure and dynamics of the nucleon has been deeply renewed with the advent of a parameterization of the partonic structure of the nucleon in terms of the Generalized Parton Distributions (GPDs). Encoding the correlations…
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks.…
In this contribution, we discuss a total cross-section model which can be applied to both photon and purely hadronic processes. We find that the model can reproduce photo-production cross-sections, as well as extrapolations of gamma* p…
We present in detail a dispersion relation formalism for virtual Compton scattering (VCS) off the proton from threshold into the $\Delta(1232)$-resonance region. Such a formalism can be used as a tool to extract the generalized…
We determine, based on the latest experimental Deep Virtual Compton Scattering experimental data, the dependence of the spatial size of the proton on the quark's longitudinal momentum. This results in a three-dimensional momentum-space…
The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error…
A method to reconstruct fields, source strengths and physical parameters based on Gaussian process regression is presented for the case where data are known to fulfill a given linear differential equation with localized sources. The…
In this contribution, we present a study of the coherent and incoherent nuclear DVCS process in the small-$x$ regime within the color dipole formalism. Predictions for the nuclear DVCS cross section at photon level in the collider…