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Collinear parton distribution functions (cPDFs) and transverse momentum dependent distributions (TMDs) are essential for calculating cross sections in high-energy physics, particularly within collinear and kt-factorization frameworks.…

高能物理 - 唯象学 · 物理学 2026-02-16 R. Kord Valeshabadi , S. Rezaie

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo…

高能物理 - 唯象学 · 物理学 2020-12-16 Marco Rossi , Stefano Carrazza , Juan M. Cruz-Martinez

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo…

高能物理 - 唯象学 · 物理学 2021-05-19 Stefano Carrazza , Juan M. Cruz-Martinez , Marco Rossi

We present a model-independent determination of the nuclear parton distribution functions (nPDFs) using machine learning methods and Monte Carlo techniques based on the NNPDF framework. The neutral-current deep-inelastic nuclear structure…

高能物理 - 唯象学 · 物理学 2020-10-28 Rabah Abdul Khalek , Jacob J. Ethier , Juan Rojo , Gijs van Weelden

We present recent results of the NNPDF collaboration on a full DIS analysis of Parton Distribution Functions (PDFs). Our method is based on the idea of combining a Monte Carlo sampling of the probability measure in the space of PDFs with…

高能物理 - 唯象学 · 物理学 2008-05-21 NNPDF Collaboration , M. Ubiali , R. D. Ball , L. Del Debbio , S. Forte , A. Guffanti , J. I. Latorre , A. Piccione , J. Rojo

The Fortran LHAPDF library has been a long-term workhorse in particle physics, providing standardised access to parton density functions for experimental and phenomenological purposes alike, following on from the venerable PDFLIB package.…

高能物理 - 唯象学 · 物理学 2015-06-23 Andy Buckley , James Ferrando , Stephen Lloyd , Karl Nordstrom , Ben Page , Martin Ruefenacht , Marek Schoenherr , Graeme Watt

We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering…

We present a new software package designed to reduce the computational burden of hadron collider measurements in Parton Distribution Function (PDF) fits. The APFELgrid package converts interpolated weight tables provided by APPLgrid files…

高能物理 - 唯象学 · 物理学 2017-01-04 Valerio Bertone , Stefano Carrazza , Nathan P. Hartland

Accurate Standard Model predictions of proton-proton collisions are essential for interpreting the current and forthcoming experimental measurements from high-energy colliders. The quest for physics beyond the Standard Model is in fact…

高能物理 - 唯象学 · 物理学 2025-04-09 Giacomo Magni

We discuss the determination of the parton substructure of hadrons by casting it as a peculiar form of pattern recognition problem in which the pattern is a probability distribution, and we present the way this problem has been tackled and…

高能物理 - 唯象学 · 物理学 2020-08-31 Stefano Forte , Stefano Carrazza

We present a determination of the parton distributions of the nucleon from a global set of hard scattering data using the NNPDF methodology: NNPDF2.0. Experimental data include deep-inelastic scattering with the combined HERA-I dataset,…

高能物理 - 唯象学 · 物理学 2014-11-20 Richard D. Ball , Luigi Del Debbio , Stefano Forte , Alberto Guffanti , Jose I. Latorre , Juan Rojo , Maria Ubiali

We present a first determination of the nuclear parton distribution functions (nPDF) based on the NNPDF methodology: nNNPDF1.0. This analysis is based on neutral-current deep-inelastic structure function data and is performed up to NNLO in…

高能物理 - 唯象学 · 物理学 2019-07-23 Rabah Abdul Khalek , Jacob J. Ethier , Juan Rojo

We present new sets of nuclear parton distribution functions (nPDFs) at next-to-leading order and next-to-next-to-leading order in perturbative QCD. Our analyses are based on deeply inelastic scattering data with charged-lepton and neutrino…

高能物理 - 唯象学 · 物理学 2022-06-09 Ilkka Helenius , Marina Walt , Werner Vogelsang

Parton Distribution Functions (PDFs) are essential non-perturbative inputs for calculation of any observable with hadronic initial states. These PDFs are released by individual groups as discrete grids as a function of the Bjorken-x and…

高能物理 - 唯象学 · 物理学 2019-10-08 D. B. Clark , E. Godat , F. I. Olness

Parton distribution functions (PDFs) are an essential ingredient for theoretical predictions at colliders. Since their exact form is unknown, their handling and delivery for practical applications relies on approximate numerical methods. We…

高能物理 - 唯象学 · 物理学 2022-04-04 Markus Diehl , Riccardo Nagar , Frank J. Tackmann

In this contribution we present a status report on the recent progress towards an analysis of nuclear parton distribution functions (nPDFs) using the NNPDF methodology. We discuss how the NNPDF fitting approach can be extended to account…

高能物理 - 唯象学 · 物理学 2018-11-15 Rabah Abdul Khalek , Jacob J. Ethier , Juan Rojo

Standard methods for higher-order calculations of QCD cross sections in hadron-induced collisions are time-consuming. The fastNLO project uses multi-dimensional interpolation techniques to convert the convolutions of perturbative…

高能物理 - 唯象学 · 物理学 2012-08-20 Daniel Britzger , Klaus Rabbertz , Fred Stober , Markus Wobisch

We present NNPDF3.0, the first set of parton distribution functions (PDFs) determined with a methodology validated by a closure test. NNPDF3.0 uses a global dataset including HERA-II deep-inelastic inclusive cross-sections, the combined…

Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and…

计算物理 · 物理学 2020-07-21 Juan M Cruz-Martinez , Stefano Carrazza , Roy Stegeman

We present the first NNPDF full set of Parton Distribution Functions from a comprehensive DIS analysis. This approach, combining a Monte Carlo sampling of the probability measure in the space of PDFs with the use of neural networks as…

高能物理 - 唯象学 · 物理学 2009-11-13 Maria Ubiali
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