Related papers: Self-Organizing Maps Parametrization of Deep Inela…
We discuss recent theoretical results on diffractive deeply inelastic scattering, focusing on the partonic picture of diffraction in configuration space and the predictions for the beta behavior and the scaling violation.
We derive mass corrections for semi-inclusive deep inelastic scattering of leptons from nucleons using a collinear factorization framework which incorporates the initial state mass of the target nucleon and the final state mass of the…
Semi-inclusive deep inelastic scattering off nuclei is a unique process to study the parton propagation mechanism and its modification induced by the presence of the nuclear medium. It allows us to probe the medium properties, particularly…
The compatibility of neutrino-nucleus deep inelastic scattering data within the universal, factorizable nuclear parton distribution functions has been studied independently by several groups in the past few years. The conclusions are…
We review the present status of polarized structure functions measured in deep-inelastic scattering. We discuss the x and Q^2 dependence of the structure function g_1, and how it can be used to test perturbative QCD at next-to-leading order…
Differential cross sections of deep inelastic scattering of charged leptons from hadrons are investigated by using the gauge/string duality. We consider vector mesons derived from different holographic dual models obtaining a general…
We give a general overview of the role and interpretation of fragmentation functions in hard processes. Transverse momentum dependence gives rise to time-reversal odd fragmentation functions contributing at leading order. Final state…
We perform a detailed study of the consistency between different sets of polarized deep inelastic scattering data and theory, from the standpoint of a next to leading order QCD global analysis, and following the criteria proposed by Collins…
Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNNs have been proposed, selecting and designing the…
In the framework of convolutional neural networks that lie at the heart of deep learning, downsampling is often performed with a max-pooling operation that only retains the element with maximum activation, while completely discarding the…
Recently dispersion relations have been applied to hard exclusive processes such as deeply virtual Compton scattering, and a holographic principle was proposed that maps out the generalized parton distributions entering the soft matrix…
This is a review of the program we started in 1968 to understand and generalize Bjorken scaling and Feynman's parton model in a canonical quantum field theory. It is shown that the parton model proposed for deep inelastic electron…
We discuss the determination of polarized parton distributions from a next-to-leading order analysis of recent experimental data. We extract the first moment of the polarized quark and gluon distribution and assess the corresponding…
Parton distributions given by deep inelastic lepton scattering (DIS) are not equal to the probabilities of finding those partons in the parent wave function. Soft rescattering of the struck parton within the coherence length of the hard…
Polynomial regression is widely used and can help to express nonlinear patterns. However, considering very high polynomial orders may lead to overfitting and poor extrapolation ability for unseen data. The paper presents a method for…
Dynamical parton densities, generated radiatively from valence-like inputs at some low resolution scale, are confronted with recent small-x data on deep inelastic and other hard scattering processes. It is shown that within theoretical…
We comprehensively studied the morphology of the self-organized effective network structures that form in simple coupled maps with interelement synchronization-dependent connection changes. Based on the parameter values, the spontaneous…
We present MAPFF1.0_Lambda, the first global analysis at next-to-next-to-leading order in perturbative QCD of the collinear unpolarised fragmentation functions of Lambda hyperons. The fit is based on data from single-inclusive…
I review the status of next-to-leading-order calculations for hadronic final states in deeply-inelastic lepton--nucleon scattering. In more detail, I focus on calculations of (2+1)-jet-type cross sections, describe recent progress in…
As gradient descent method in deep learning causes a series of questions, this paper proposes a novel gradient-free deep learning structure. By adding a new module into traditional Self-Organizing Map and introducing residual into the map,…