相关论文: Deep Inelastic Structure Functions in Bag-Like Mod…
Nuclear density functional theory (DFT) is one of the main theoretical tools used to study the properties of heavy and superheavy elements, or to describe the structure of nuclei far from stability. While on-going efforts seek to better…
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior. However,…
We calculate twist--4 coefficient functions for the deep inelastic structure function $F_2(x,Q^2)$ associated to 4--gluon operator matrix elements for general values of the Bjorken variable $x$ and study the numerical effect on the slope…
Nuclear shadowing corrections to the structure functions of deep inelastic scattering of intermediate-mass nuclei are calculated at very low values of Bjorken x and small values of Q^2 (Q^2<5 GeV^2). The two-component approach (generalized…
We introduce a perturbative model that accounts for the contribution of multi-partonic interactions to collider observables. A key feature of this multi-parton model is that cross sections are organised in terms of building blocks that are…
The nucleon structure function $F_2^N$ computed in a holographic framework can be used to describe nuclear deep inelastic scattering effects provided that a rescaling of the $Q^2$ momentum and of the IR hard-wall parameter $z_0$ is made.…
On the basis of the target mass corrections to structure functions of deep-inelastic scattering of leptons, we evaluate effective nucleon mass that turns out to be twice $M_{nucl.}$ for deep-inelastic scattering on the nucleus target and…
The increasingly wide use of deep machine learning techniques in computational mechanics has significantly accelerated simulations of problems that were considered unapproachable just a few years ago. However, in critical applications such…
A brief review of the SIESTA project is presented in the context of linear-scaling density-functional methods for electronic-structure calculations and molecular-dynamics simulations of systems with a large number of atoms. Applications of…
Proteins are the fundamental macromolecules that play diverse and crucial roles in all living matter and have tremendous implications in healthcare, manufacturing, and biotechnology. Their functions are largely determined by the sequences…
The off-mass-shell behavior of the nucleon structure function, $F_2^N$, is studied within an approach motivated by the Sullivan model. Deep inelastic scattering on the nucleon is considered in the second order in the pion-nucleon coupling…
We study the nuclear effects in the electromagnetic structure function F2(x,Q^2) in nuclei in the deep inelastic lepton nucleus scattering process by taking into account Fermi motion, binding, pion and rho meson cloud contributions.…
A recent reanalysis of world proton and deuteron structure function measurements showed that a significant amount of the apparent model dependence in the extraction of the neutron structure function was related to inconsistencies between…
The $F_2$, $F_G$, $R = F_L/F_T$ proton structure functions are derived in the QCD dipole picture. Assuming $k_T$ and renormalization-group factorization, we relate deep-inelastic proton scattering to deep-inelastic onium scattering. We get…
We present work in a model used to describe semi-inclusive deep inelastic scattering off the deuteron. The model uses the virtual nucleon approximation to describe the interaction of the photon with the bound neutron and the generalized…
We report on a high statistics quenched lattice QCD calculation of the deep-inelastic structure functions $F_1$, $F_2$, $g_1$ and $g_2$ of the proton and neutron. The theoretical basis for the calculation is the operator product expansion.…
We describe a high statistics quenched QCD calculation of the moments of the polarized deep-inelastic structure functions g_1 and g_2 of the proton and neutron.
We present parton distribution functions which include a quantitative estimate of its uncertainties. The parton distribution functions are optimized with respect to deep inelastic proton data, expressing the uncertainties as a density…
Covariance functions are a fundamental tool for modeling the dependence structure of spatial processes. This work investigates novel constructions for covariance functions that enable the integration of anisotropies and hole effects in…
We introduce a new higher categorical structure called a weakly globular n-fold category. This structure is based on iterated internal categories and on the notion of weak globularity. We identify a suitable class of pseudo-functors whose…