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Area of Science:

  • High Energy Physics
  • Computational Physics

Background:

  • Current PDF4LHC recommendations require combining multiple PDF sets, which is computationally intensive.
  • Existing tools often lack universal PDF uncertainty evaluation, necessitating complex post-analysis combinations.

Purpose of the Study:

  • To develop a strategy for statistically combining and compressing PDF sets.
  • To reduce the computational overhead associated with PDF uncertainty estimation in LHC physics.

Main Methods:

  • Utilized Monte Carlo (MC) representation of Hessian PDF sets.
  • Implemented a compression algorithm to reduce the number of MC replicas.
  • Combined and compressed NNPDF3.0, CT14, and MMHT14 NNLO PDF sets.

Main Results:

  • Demonstrated a method for combining and compressing diverse PDF sets.
  • Validated the compressed sets using parton luminosities and LHC cross sections.
  • Determined that approximately 100 MC replicas adequately represent the combined PDF set.

Conclusions:

  • The proposed strategy offers an efficient approach to PDF uncertainty estimation.
  • Compressed MC PDF sets are suitable for general LHC phenomenology.
  • This method significantly reduces computational resources for theoretical predictions.