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Nuclear_Corrections

This project is about the inclusion of nuclear corrections for fixed-target experiments into a typical NNPDF fit.

Table of contents

  • Papers

    contains selected papers useful for this project

  • LHAPDF_Production

    contains the code to generate appropriate LHAPDF grids for the DSSZ and EPPS nuclear PDF sets

  • MonteCarlo

    contains the code and the runcard to generate the Monte Carlo sets for each Hessian set of nuclear PDFs

  • nPDFsets

    contains the nuclear PDF sets produced by the bits of code in LHAPDF_Production and MonteCarlo

  • Observables

    contains the validphys folder to generate predictions - the NUCLEAR branch of the NNPDF code must be installed

  • Covariance

    contains the code to generate the additional nuisance parameters associated to the nuclear corrections

  • Plotting_code

    contains the code that generates plots for the paper

  • Strangeness_fraction

    contains the code to compute the ratios Rs and Ks

Workflow

  • Generate the appropriate Hessian grids for the nuclear PDF sets not available in the proper LHAPDF format by running the code in LHAPDF_Production.

  • Generate the appropriate Monte Carlo sets for the nuclear PDF sets in the LHAPDF format by running the code in MonteCarlo

  • For convenience, the nuclear PDF sets generated above are stored in nPDFsets. Please consider to skip the two first steps in this list and just copy these LHAPDF grids into your local LHAPDF intallation path.

  • Compute the observables for the nuclear experiments by running the code in Observables/validphys/ for each . A complete installation of nnpdf and validphys is required.

  • Compute the nuisance parameters associated to the extra nuclear uncertainties and/or the shifts by running nuisance_new and Nuisancenucl_new in Covariance/src.

  • The output of the previous point is implemented in the NUCLEAR branch of buildmaster and in the NUCLEAR branch of nnpdf.

  • Compute (and check) the nuclear covariance matrix (uncorrelated and correlated) by running check_covariance in Covariance/src.

  • Postprocess the results by running the code in Plotting_code.