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Journal of Physics G Nuclear and Particle Physics· 2016Q1

PDF4LHC recommendations for LHC Run II

J. M. Butterworth, Stefano Carrazza, A. M. Cooper-Sarkar, A. De Roeck et al.

Short summary

Updated recommendations for parton distribution functions (PDFs) and their uncertainties are provided for LHC Run II applications, incorporating new data from LHC Run I.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Provides updated recommendations for PDF usage and uncertainty assessment for LHC Run II.
  • Incorporates new experimental data from LHC Run I into PDF sets.
  • Proposes a new prescription for combining a subset of available PDF sets into a single combined set.
  • Discusses tools for delivering combined PDF sets as Hessian eigenvectors or Monte Carlo replicas.

AI-generated from the title and abstract; the full text is not read.

Abstract

We provide an updated recommendation for the usage of sets of parton distribution functions (PDFs) and the assessment of PDF and PDF+${\alpha }_{s}$ uncertainties suitable for applications at the LHC Run II. We review developments since the previous PDF4LHC recommendation, and discuss and compare the new generation of PDFs, which include substantial information from experimental data from the Run I of the LHC. We then propose a new prescription for the combination of a suitable subset of the available PDF sets, which is presented in terms of a single combined PDF set. We finally discuss tools which allow for the delivery of this combined set in terms of optimized sets of Hessian eigenvectors or Monte Carlo replicas, and their usage, and provide some examples of their application to LHC phenomenology. This paper is dedicated to the memory of Guido Altarelli (1941–2015), whose seminal work made possible the quantitative study of PDFs.

The authors' abstract, as published at the source. Journal of Physics G Nuclear and Particle Physics, 2016 · DOI ↗

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Field: Nuclear and High Energy Physics

Nuclear and High Energy PhysicsPhysics and Astronomy