Related papers: Deep Inelastic Structure Functions in Bag-Like Mod…
We compute the lower moments of the structure functions of $\pi$ and $\rho$. Of particular interest are the spin-dependent structure functions of the $\rho$ as they give new information about quark binding effects.
There has recently been surprising progress in understanding the spin and flavor dependence of deep inelastic structure functions in terms of the same physics needed in the simple quark models used for hadronic spectroscopy. However, the…
We propose a metric learning framework for the construction of invariant geometric functions of planar curves for the Eucledian and Similarity group of transformations. We leverage on the representational power of convolutional neural…
Log-linear models are a family of probability distributions which capture relationships between variables. They have been proven useful in a wide variety of fields such as epidemiology, economics and sociology. The interest in using these…
Hybrid density functional calculation is indispensable to accurate description of electronic structure, whereas the formidable computational cost restricts its broad application. Here we develop a deep equivariant neural network method…
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and…
In order to obtain polarised parton densities we have made next to leading order QCD fit using experimental data on deep inelastic structure functions on nucleons. This fit is compared with the updated fit to coresponding spin asymmetries.…
The spin-dependent structure function of the deuteron, g_1D, is calculated within a covariant framework. The off-shell structure of the bound nucleon gives corrections to the convolution model at a level of half a percent for x below 0.7,…
The spin dependent structure functions, g$_{1p}$ of the proton and g$_{1n}$ of the neutron, calculated in the nucleon rest frame using a relativistic quark model wave function, are compared with recent experiments.
We present the first experiment-based parameterization of differential gluon structure function, which is called upon in many applications. We compare $\kappa$-factorization and DGLAP approaches and analyze properties of DGSF, with special…
We provide simple schemes to build Bayesian Neural Networks (BNNs), block by block, inspired by a recent idea of computation skeletons. We show how by adjusting the types of blocks that are used within the computation skeleton, we can…
In this paper we formulate the theory of nonlinear elasticity in a geometrically intrinsic manner using exterior calculus and bundle-valued differential forms. We represent kinematics variables, such as velocity and rate-of-strain, as…
We derive the analytic continuation of the Mellin moments of deep inelastic structure functions at the next-to-next-to-leading order accuracy.
Based on a simple model which is compatible with the idea of the static quark model and the parton model, the polarized structure functions of proton and deuteron, two-spin asymmetries of $\pi ^0$ in polarized $pp$ reactions and inelastic…
In this work, we extend the existing framework of inelastic constitutive artificial neural networks (iCANNs) by incorporating plasticity to increase their applicability to model more complex material behavior. The proposed approach ensures…
Deep learning model design, development, and debugging is a process driven by best practices, guidelines, trial-and-error, and the personal experiences of model developers. At multiple stages of this process, performance and internal model…
The inelasticity in nucleus-nucleus collisions at high energies is calculated in the framework of geometrical multichain model. The very fast increase of the inelasticity is found as a result of a second-stage cascading process. The same…
Parametric factor copula models typically work well in modeling multivariate dependencies due to their flexibility and ability to capture complex dependency structures. However, accurately estimating the linking copulas within these models…
We analyze power corrections to flavour singlet deep inelastic scattering structure functions in the framework of the infrared renormalon model. Our calculations, together with previous results for the non-singlet contribution, allow to…
The DIS process on nuclei is considered in the framework of LO BK equation with local impact parameter dependence. Solving BK equation parameters for initial conditions of GBW type found in \cite{Serg1} were used. Integrated gluon density…