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We present a comprehensive new global QCD analysis of polarized inclusive deep-inelastic scattering, including the latest high-precision data on longitudinal and transverse polarization asymmetries from Jefferson Lab and elsewhere. The…

High Energy Physics - Phenomenology · Physics 2016-04-13 Nobuo Sato , W. Melnitchouk , S. E. Kuhn , J. J. Ethier , A. Accardi

Computer simulation has become one of the most important tools in scientific research in many disciplines. Benefiting from the dynamical trajectories regulated by versatile interatomic interactions, various material properties can be…

Materials Science · Physics 2024-11-28 Y. -C. Hu , J. Tian

pyGDM is a python toolkit for electro-dynamical simulations in nano-optics based on the Green Dyadic Method (GDM). In contrast to most other coupled-dipole codes, pyGDM uses a generalized propagator, which allows to cost-efficiently solve…

Computational Physics · Physics 2020-01-28 Peter R. Wiecha

PySEMTools is a Python-based library for post-processing simulation data produced with high-order hexahedral elements in the context of the spectral element method in computational fluid dynamics. It aims to minimize intermediate steps…

Computational Physics · Physics 2025-04-18 Adalberto Perez , Siavash Toosi , Tim Felle Olsen , Stefano Markidis , Philipp Schlatter

We consider semi-inclusive deep inelastic scattering (SIDIS) and Drell-Yan events within transverse momentum dependent (TMD) factorization. Based on the simultaneous fit of multiple data points, we extract the unpolarized TMD distributions…

High Energy Physics - Phenomenology · Physics 2020-07-15 Ignazio Scimemi , Alexey Vladimirov

We present MAPFF1.0, a determination of unpolarised charged-pion fragmentation functions (FFs) from a set of single-inclusive $e^+e^-$ annihilation and lepton-nucleon semi-inclusive deep-inelastic-scattering (SIDIS) data. FFs are…

High Energy Physics - Phenomenology · Physics 2021-08-18 Rabah Abdul Khalek , Valerio Bertone , Emanuele R. Nocera

We present a Fortran 77/95 code capable of computing QCD corrections in deep inelastic scattering (DIS). The code uses the Projection-to-Born method to augment an existing $\mathcal{O}(\alpha_s^2)$ dijet DIS code, thereby obtaining…

High Energy Physics - Phenomenology · Physics 2024-07-31 Alexander Karlberg

The udkm1Dsim toolbox is a collection of Python classes and routines to simulate the thermal, structural, and magnetic dynamics after laser excitation as well as the according X-ray scattering response in one-dimensional sample structures.…

Computational Physics · Physics 2021-05-31 Daniel Schick

We present a first global determination of spin-dependent parton distribution functions (PDFs) and their uncertainties using the NNPDF methodology: NNPDFpol1.1. Longitudinally polarized deep-inelastic scattering data, already used for the…

High Energy Physics - Phenomenology · Physics 2015-06-22 Emanuele R. Nocera , Richard D. Ball , Stefano Forte , Giovanni Ridolfi , Juan Rojo

We present the software design of Gridap, a novel finite element library written exclusively in the Julia programming language, which is being used by several research groups world-wide to simulate complex physical phenomena such as…

Mathematical Software · Computer Science 2022-04-13 Francesc Verdugo , Santiago Badia

Since Lorenz's seminal work on a simplified weather model, the numerical analysis of nonlinear dynamical systems has become one of the main subjects of research in physics. Despite of that, there remains a need for accessible, efficient,…

We present the Maple package TDDS (Thomas Decomposition of Differential Systems). Given a polynomially nonlinear differential system, which in addition to equations may contain inequations, this package computes a decomposition of it into a…

Computational Physics · Physics 2018-11-14 Vladimir P. Gerdt , Markus Lange-Hegermann , Daniel Robertz

We consider some trends, achievements and a series of remaining problems in the precision determination of parton distribution functions. For the description of the scaling violations of the deep-inelastic scattering data, forming the key…

High Energy Physics - Phenomenology · Physics 2018-09-05 S. Alekhin , J. Bluemlein , S. -O. Moch

We report on recent results obtained for the 3-loop heavy flavor Wilson coefficients in deep-inelastic scattering (DIS) at general values of the Mellin variable $N$ at larger scales of $Q^2$. These concern contributions to the gluonic…

High Energy Physics - Phenomenology · Physics 2013-01-03 J. Ablinger , J. Blümlein , A. De Freitas , A. Hasselhuhn , S. Klein , C. Schneider , F. Wißbrock

Deeply inelastic scattering (DIS) is a powerful probe for investigating the QCD structure of hadronic matter and testing the standard model (SM). DIS can be described through QCD factorization theorems which separate contributions to the…

High Energy Physics - Phenomenology · Physics 2025-03-21 Henry Bloss , Brandon Kriesten , T. J. Hobbs

Inspired by a recent study of Iancu, Mueller and Triantafyllopoulos [1] and earlier papers by Golec-Biernat and Wusthoff [2,3], we propose semi-inclusive diffractive deep inelastic scattering (SIDDIS) to investigate the gluon tomography in…

High Energy Physics - Phenomenology · Physics 2022-11-23 Yoshitaka Hatta , Bo-Wen Xiao , Feng Yuan

The neutrino deep inelastic scattering (DIS) data is very interesting for global analyses of proton and nuclear parton distribution functions (PDFs) since they provide crucial information on the strange quark distribution in the proton and…

High Energy Physics - Phenomenology · Physics 2011-11-07 K. Kovarik , I. Schienbein , F. I. Olness , J. Y. Yu , C. Keppel , J. G. Morfin , J. F. Owens , T. Stavreva

Experimental measurements in deep-inelastic scattering and lepton-pair production on deuterium targets play an important role in the flavor separation of $u$ and $d$ (anti)quarks in global QCD analyses of the parton distribution functions…

High Energy Physics - Phenomenology · Physics 2021-07-21 A. Accardi , T. J. Hobbs , X. Jing , P. M. Nadolsky

Dimensionality reduction (DR) techniques inherently distort the original structure of input high-dimensional data, producing imperfect low-dimensional embeddings. Diverse distortion measures have thus been proposed to evaluate the…

Machine Learning · Computer Science 2023-08-14 Hyeon Jeon , Aeri Cho , Jinhwa Jang , Soohyun Lee , Jake Hyun , Hyung-Kwon Ko , Jaemin Jo , Jinwook Seo