Related papers: Self-Organizing Maps and Parton Distributions Func…
Following a previous detailed study of unpolarized generalized parton distribution functions in the non-singlet sector, and at zero values of the skewness variable, $\zeta$, we propose a physically motivated parametrization that is valid at…
We construct a model for inelastic neutrino- and electron-nucleon scattering cross sections using effective leading order parton distribution functions with a new scaling variable $\xi_w$. Non-perturbative effects are well described using…
An introductory to generalized parton distributions is given which emphasizes their spectral property and its uses as well as the equivalence of various GPD representations. Furthermore, the status of the theory and phenomenology of hard…
We review several recent developments in the theory and phenomenology of deep-inelastic scattering, with particular emphasis on precision tests of QCD and progress in the detailed perturbative treatment of structure functions and parton…
We discuss a diffusion based implementation of the self-organizing map on the unit hypersphere. We show that this approach can be efficiently implemented using just linear algebra methods, we give a python numpy implementation, and we…
In this work, we present the development of a neuro-inspired approach for characterizing sensorimotor relations in robotic systems. The proposed method has self-organizing and associative properties that enable it to autonomously obtain…
The computation of the parton distribution functions (PDF) or distribution amplitudes (DA) of hadrons from first principles lattice QCD constitutes a central open problem. In this study, we present and evaluate the efficiency of a selection…
A non-perturbative approach to Generalized Parton Distributions, and to Deeply Virtual Compton Scattering in particular, based on off mass shell extension of dual amplitudes with Mandelstam analyticity (DAMA) is developed with the spin and…
We analyze different prescriptions for the inclusion of target mass effects in the extraction of parton distributions from the measured structure functions. As a main result, the problem of defining parton distributions in the presence of…
Exploration is a fundamental problem in robotics. While sampling-based planners have shown high performance, they are oftentimes compute intensive and can exhibit high variance. To this end, we propose to directly learn the underlying…
We propose a physically motivated parametrization for the unpolarized generalized parton distributions, H and E, valid at both zero and non-zero values of the skewness variable, \zeta. Our approach follows a previous detailed study of the…
A novel approach to parton distributions parameterization in terms of quantum statistical functions is here outlined. The description, already proposed in previous publications, is here improved by adding to the statistical distributions an…
In the inverse problem in particle physics, given an unexpected observation, one aims to identify a unique choice from amongst several competing hypotheses. We explore a novel approach of applying self-organizing maps to the inverse problem…
The impact of recent precision measurements of DIS structure functions and inclusive jet production at the Tevatron on the global QCD analysis of parton distribution functions is studied in detail. Particular emphasis is placed on exploring…
The current understanding of deep neural networks can only partially explain how input structure, network parameters and optimization algorithms jointly contribute to achieve the strong generalization power that is typically observed in…
We update the earlier work of Refs. arXiv:1703.08562 and arXiv:1712.07147 on parton distribution functions in the full Standard Model to include gauge boson polarization, non-zero input electroweak boson PDFs and next-to-leading-order…
Determinations of structure functions and parton distribution functions have been recently obtained using Monte Carlo methods and neural networks as universal, unbiased interpolants for the unknown functional dependence. In this work the…
We investigate the feasibility of constraining parton distribution functions in the proton through a comparison with data on semi-inclusive deep-inelastic lepton-nucleon scattering. Specifically, we reweight replicas of these distributions…
The spatial distribution of partons in the proton can be probed in suitable exclusive scattering processes. I report on recent performance estimates for parton imaging at a proposed Electron-Ion Collider.
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…