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Updated: Feb 20, 2026

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
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hoppet v2 release note.

Alexander Karlberg1, Paolo Nason2, Gavin Salam3,4

  • 1CERN, Theoretical Physics Department, 1211 Geneva 23, Switzerland.

The European Physical Journal. C, Particles and Fields
|February 19, 2026
PubMed
Summary
This summary is machine-generated.

The hoppet code now supports higher-order Quantum Chromodynamics (QCD) and Quantum Electrodynamics (QED) evolution, enhancing precision for parton distribution functions. New features improve usability and performance for hadronic structure function determination.

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Area of Science:

  • High Energy Physics
  • Quantum Chromodynamics
  • Quantum Electrodynamics

Background:

  • Parton distribution functions (PDFs) are essential for understanding the internal structure of hadrons.
  • Accurate PDF evolution is crucial for interpreting results from high-energy particle collider experiments.

Purpose of the Study:

  • To document new features in the v2 release of the hoppet code.
  • To enhance the precision and capabilities of PDF evolution calculations.
  • To improve the usability and performance of the hoppet code.

Main Methods:

  • Implementation of N3LO QCD evolution in the variable flavor number scheme.
  • Inclusion of QED evolution to phenomenologically NNLO QCD equivalent accuracy.
  • Development of a new Python interface and CMake build option.
  • Addition of functionality to save hoppet tables as LHAPDF grids.

Main Results:

  • The hoppet code now supports N3LO QCD and QED evolution.
  • Determination of hadronic structure functions for massless quarks is now possible up to N3LO.
  • Improved performance benchmarks demonstrate optimized interpolation of PDF tables.
  • Enhanced code usability through a new Python interface and LHAPDF grid compatibility.

Conclusions:

  • The v2 release of hoppet significantly advances the capabilities for precise PDF evolution.
  • These updates facilitate more accurate theoretical predictions for high-energy physics phenomenology.
  • The new features and optimizations make hoppet a more powerful and versatile tool for researchers.