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Related papers: New analysis for Nucleon Form Factors from GPDs

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Domain adaptation remains a challenge when there is significant manifold discrepancy between source and target domains. Although recent methods leverage manifold-aware adversarial perturbations to perform data augmentation, they often…

Computer Vision and Pattern Recognition · Computer Science 2025-05-22 Hana Satou , F Monkey

Existing pion+nucleus Drell-Yan and electron+pion scattering data are used to develop ensembles of model-independent representations of the pion generalised parton distribution (GPD). Therewith, one arrives at a data-driven prediction for…

High Energy Physics - Phenomenology · Physics 2023-04-12 Yin-Zhen Xu , Khépani Raya , Zhu-Fang Cui , Craig D. Roberts , J. Rodríguez-Quintero

Results from a recent analysis of the zero-skewness generalized parton distributions (GPDs) for valence quarks are discussed. The analysis bases on a physically motivated parameterization of the GPDs with a few free parameters adjusted to…

High Energy Physics - Phenomenology · Physics 2025-01-22 P. Kroll

The folding of RNA and DNA strands plays crucial roles in biological systems and bionanotechnology. However, studying these processes with high-resolution numerical models is beyond current computational capabilities due to the timescales…

Soft Condensed Matter · Physics 2024-02-07 F. Tosti Guerra , E. Poppleton , P. Šulc , L. Rovigatti

When working with large biological data sets, exploratory analysis is an important first step for understanding the latent structure and for generating hypotheses to be tested in subsequent analyses. However, when the number of variables is…

Methodology · Statistics 2017-02-03 Julia Fukuyama

Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more…

Machine Learning · Statistics 2024-07-11 Puoya Tabaghi , Michael Khanzadeh , Yusu Wang , Sivash Mirarab

We propose a new fast generalized functional principal components analysis (fast-GFPCA) algorithm for dimension reduction of non-Gaussian functional data. The method consists of: (1) binning the data within the functional domain; (2)…

Methodology · Statistics 2023-06-06 Andrew Leroux , Ciprian Crainiceanu , Julia Wrobel

Generalized parton distributions (GPDs) are 3-dimensional (3D) structure functions for hadrons, and they are important for solving the proton spin puzzle including partonic orbital-angular-momentum contributions. The $s$-$t$ crossed…

High Energy Physics - Phenomenology · Physics 2019-02-13 S. Kumano , Qin-Tao Song , O. V. Teryaev

It is well established now that the generalized parton distributions (GPDs) at zero skewness are playing important roles in some physical process such as elastic electron-nucleon scattering, elastic (anti)neutrino-nucleon scattering, and…

High Energy Physics - Phenomenology · Physics 2024-05-02 The MMGPDs Collaboration , Muhammad Goharipour , Hadi Hashamipour , Fatemeh Irani , K. Azizi

We determine the properties of generalised parton distributions (GPDs) from a lattice QCD calculation of the off-forward Compton amplitude (OFCA). By extending the Feynman-Hellmann relation to second-order matrix elements at off-forward…

We extract polarized parton distribution functions (PPDFs), referred to as "KTA17," together with the highly correlated strong coupling $\alpha_s$ from recent and up-to-date $g_1$ and $g_2$ polarized structure functions world data at…

High Energy Physics - Phenomenology · Physics 2017-04-07 Hamzeh Khanpour , Sara Taheri Monfared , S. Atashbar Tehrani

In the framework of the new t-dependence of the General Parton Distributions (GPDs), which reproduce the electromagnetic form factors of the proton and neutron at small and large momentum transfer, the gravitational form factors of the…

High Energy Physics - Phenomenology · Physics 2008-10-06 O. V. Selyugin , O. V. Teryaev

We revisit the model for parametrization of momentum dependence of nucleon generalized parton distributions in the light of recent MRST measurements of parton distribution functions. Our parametrization method with minimum set of free…

High Energy Physics - Phenomenology · Physics 2016-12-21 Neetika Sharma

The new parameterization of the generalized parton distributions t-dependence is proposed. It allows one to reproduce sufficiently well the electromagnetic form factors of the proton and neutron at small and large momentum transfer. The…

High Energy Physics - Phenomenology · Physics 2009-02-20 O. V. Selyugin , O. V. Teryaev

The elastic neutron form factors $G_{En}$ and $G_{Mn}$ are calculated in a GPD framework using GPDs obtained from fits to proton elastic form factors $G_{Ep}$ and $G_{Mp}$, and isospin symmetry, with no further changes in parameters. The…

High Energy Physics - Phenomenology · Physics 2007-05-23 Paul Stoler

The measurement of nuclear Generalized Parton Distributions (GPDs) will represent a valuable tool to understand the structure of bound nucleons in the nuclear medium, as well as the role of non-nucleonic degrees of freedom in the…

Nuclear Theory · Physics 2009-03-04 S. Scopetta

Training data attribution (TDA) methods ask which training documents are responsible for a model behavior. However, models often learn broad concepts shared across many examples. Moreover, existing TDA methods are supervised -- they require…

Artificial Intelligence · Computer Science 2026-03-18 J Rosser

Generalized parton distributions (GPDs) have been investigated in the deeply virtual Compton scattering (DVCS) to solve the proton spin puzzle. On the other hand, the generalized distribution amplitudes (GDAs) can be studied in the…

High Energy Physics - Phenomenology · Physics 2018-08-21 Qin-Tao Song

A complete description of the nucleon structure in terms of generalized parton distributions (GPDs) at twist 2 level requires the measurement/computation of the eight functions H, E, \tilde H, \tilde E, H_T, E_T, \tilde H_T and \tilde E_T,…

High Energy Physics - Phenomenology · Physics 2008-11-26 Ph. Hagler , J. W. Negele , D. B. Renner , W. Schroers , T. Lippert , K. Schilling

Modelling and understanding properties of materials from first principles require knowledge of the underlying atomistic structure. This entails knowing the individual identity and position of all involved atoms. Obtaining such information…

Chemical Physics · Physics 2023-07-06 Mads-Peter Verner Christiansen , Nikolaj Rønne , Bjørk Hammer