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相关论文: PDFFlow: parton distribution functions on GPU

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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

In this proceedings we describe the computational challenges associated to the determination of parton distribution functions (PDFs). We compare the performance of the convolution of the parton distributions with matrix elements using…

高能物理 - 唯象学 · 物理学 2019-09-25 Stefano Carrazza , Juan Cruz-Martinez , Jesús Urtasun-Elizari , Emilio Villa

Study of parton distribution functions (PDFs) has led to a finer cognisance of the structure of partons in hadrons and the proton structure functions in deep inelastic scattering (DIS). PDFs are instrumental in predicting results for most…

高能物理 - 唯象学 · 物理学 2025-10-02 Akbari Jahan , Diptimonta Neog

We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration techniques designed for platforms with hardware accelerators. The growing complexity of calculations and simulations in…

计算物理 · 物理学 2020-06-24 Stefano Carrazza , Juan M. Cruz-Martinez

The interest into parton distribution functions (PDFs) and fragmentation functions (FFs) in current high energy physics research is twofold. On the one hand, they are fundamental objects to conduct precision phenomenology studies, e.g. at…

高能物理 - 唯象学 · 物理学 2025-09-22 Tanishq Sharma

We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The…

计算物理 · 物理学 2021-08-18 Stefano Carrazza , Juan Cruz-Martinez , Marco Rossi , Marco Zaro

We present NeoPDF, an interpolation library that supports both collinear and transverse momentum-dependent parton distribution functions. NeoPDF is designed to be fast and reliable, with modern functionalities that target both current and…

高能物理 - 唯象学 · 物理学 2025-10-07 Tanjona R. Rabemananjara

The parton distribution functions (PDFs) of the proton, a necessary input to almost all theory predictions for hadron colliders, are reviewed in this document. An introduction to the PDF determination by global analyses of the main PDF…

高能物理 - 唯象学 · 物理学 2019-08-14 Ringaile Placakyte

Parton distribution functions play a pivotal role in hadron collider phenomenology. They are non-perturbative quantities extracted from fits to available data, and their scale dependence is dictated by the DGLAP evolution equations. In this…

We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code…

A short review of the currently available modern parton distribution functions (PDFs)and the theory predictions obtained using those PDFs for several benchmark processes at LHC, including Higgs boson production, is presented in this…

高能物理 - 唯象学 · 物理学 2016-09-23 Ringaile Placakyte

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

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

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

Perturbative quantum chromodynamics (QCD) ceases to be applicable at low interaction energies due to the rapid increase of the strong coupling. In that limit, the non-perturbative regime determines the properties of quarks and gluons…

高能物理 - 唯象学 · 物理学 2021-10-06 Rabah Abdul Khalek

Parton distribution functions (PDFs) describe universal properties of bound states and allow us to calculate scattering amplitudes in processes with large momentum transfer. Calculating PDFs involves the evaluation of matrix elements with a…

高能物理 - 格点 · 物理学 2025-02-10 Mari Carmen Bañuls , Krzysztof Cichy , C. -J. David Lin , Manuel Schneider

Transverse and longitudinal electroweak gauge boson parton distribution functions (PDFs) are computed in terms of deep-inelastic scattering structure functions, following the recently developed method to determine the photon PDF. The…

高能物理 - 唯象学 · 物理学 2018-05-18 Bartosz Fornal , Aneesh V. Manohar , Wouter J. Waalewijn

In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In order to automate MC simulation for a generic number of…

计算物理 · 物理学 2021-09-08 Stefano Carrazza , Juan Cruz-Martinez , Marco Rossi , Marco Zaro

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 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
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