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In fields such as hydrology and climatology, modelling the entire distribution of positive data is essential, as stakeholders require insights into the full range of values, from low to extreme. Traditional approaches often segment the…

Methodology · Statistics 2025-10-03 Carlo Gaetan , Philippe Naveau

We present an exploratory study using generalized parton distributions of several observables related to transverse degrees of freedom in hadronic structure -- the nucleon transverse momentum, the transverse radius, or impact parameter, and…

High Energy Physics - Phenomenology · Physics 2009-11-11 S. Liuti

AI/ML informed Symbolic Regression is the next stage of scientific modeling. We utilize a highly customizable symbolic regression package ``PySR" to model the $x$ and $t$ dependence of the flavor isovector combination $H_{u-d}(x,t,\xi)$ at…

We investigate the off-shell generalized parton distributions (GPDs) {and transverse momentum dependent parton distributions (TMDs)} of kaons within the framework of the Nambu--Jona-Lasinio model, employing proper time regularization.…

High Energy Physics - Phenomenology · Physics 2025-11-04 Jin-Li Zhang

Generalized parton distributions involving transverse polarization are transversely deformed. The deformation of chirally odd GPDs is related to a transversity decomposition of the quark angular momentum. Potential consequences for T-odd…

High Energy Physics - Phenomenology · Physics 2017-08-23 Matthias Burkardt

We sketch here an approach to the computation of generalised parton distributions (GPDs), based upon a rainbow-ladder (RL) truncation of QCD's Dyson-Schwinger equations and exemplified via the pion's valence dressed-quark GPD, $H_\pi^{\rm…

High Energy Physics - Phenomenology · Physics 2015-09-02 L. Chang , C. Mezrag , H. Moutarde , C. D. Roberts , J. Rodríguez-Quintero , F. Sabatié

We present the first calculation of the $x$-dependence of the proton generalized parton distributions (GPDs) within lattice QCD. Results are obtained for the isovector unpolarized and helicity GPDs. We compute the appropriate matrix…

In this article, a generalized inverse xgamma distribution (GIXGD) has been introduced as the generalized version of the inverse xgamma distribution. The proposed model exhibits the pattern of non-monotone hazard rate and belongs to family…

Methodology · Statistics 2018-12-13 Harsh Tripathi , Abhimanyu Singh Yadav , Mahendra Saha , Sumit Kumar

We discuss the links between Generalized Parton Distributions (GPDs) and elastic nucleon form factors. These links, in the form of sum rules, represent powerful constraints on parametrizations of GPDs. A Regge parametrization for GPDs at…

High Energy Physics - Phenomenology · Physics 2009-08-06 M. Guidal , M. V. Polyakov , A. V. Radyushkin , M. Vanderhaeghen

The position-based dynamics (PBD) algorithm is a popular and versatile technique for real-time simulation of deformable bodies, but is only applicable to forces that can be expressed as linearly compliant constraints. In this work, we…

Graphics · Computer Science 2025-12-01 Manas Chaudhary , Chandradeep Pokhariya , Rahul Narain

We present a first calculation of the generalized parton distributions of the photon(both polarized and unpolarized) using overlaps of light-front wave functions at leading order in \alpha and zeroth order in \alpha_s; for non-zero…

High Energy Physics - Phenomenology · Physics 2015-05-28 A. Mukherjee , S. Nair

Gaussian Process (GP) models are a powerful tool in probabilistic machine learning with a solid theoretical foundation. Thanks to current advances, modeling complex data with GPs is becoming increasingly feasible, which makes them an…

Machine Learning · Computer Science 2025-03-04 Sarem Seitz

Spin and transverse momentum dependent parton distributions - Generalized Parton Distributions (GPDs) - are at the interface between the QCD structure of the hadrons and observable quantities. The GPDs are linear superpositions within…

High Energy Physics - Phenomenology · Physics 2015-10-27 Gary R. Goldstein , Simonetta Liuti

We discuss a new leading-order parameterization of generalized parton distributions of the proton, which is based on the idea of duality. In its minimal version, the parameterization is defined by the usual quark singlet parton…

High Energy Physics - Phenomenology · Physics 2009-01-07 V. Guzey , M. V. Polyakov

In this paper we give a brief review of semiparametric theory, using as a running example the common problem of estimating an average causal effect. Semiparametric models allow at least part of the data-generating process to be unspecified…

Methodology · Statistics 2017-09-20 Edward H. Kennedy

We introduce the neural network approach to global fits of parton distrubution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and…

High Energy Physics - Phenomenology · Physics 2019-08-14 Andrea Piccione , Joan Rojo

We report on the extraction of the target mass contributions to the unpolarized proton structure functions by applying an unfolding procedure to the available world data from charged lepton scattering. The method employed is complementary…

High Energy Physics - Phenomenology · Physics 2012-01-13 M. E. Christy , J. Blumlein , H. Bottcher

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…

High Energy Physics - Phenomenology · Physics 2009-09-28 Saeed Ahmad , Heli Honkanen , Simonetta Liuti , Swadhin K. Taneja

Coherent Deeply virtual Compton scattering off the $^4$He nucleus is studied in impulse approximation. A convolution formula for the nuclear Generalized Parton Distribution (GPD) is derived in terms of the $^4$He non-diagonal spectral…

Nuclear Theory · Physics 2018-07-18 Sara Fucini , Sergio Scopetta , Michele Viviani

This paper puts forward a new generalized polynomial dimensional decomposition (PDD), referred to as GPDD, comprising hierarchically ordered measure-consistent multivariate orthogonal polynomials in dependent random variables. Unlike the…

Numerical Analysis · Mathematics 2018-10-30 Sharif Rahman
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