Generic Processes & Materials - Workplan 2021

  • Fast simulation:
  • modernisation of EM shower parametrisation, including automated tuning procedures
  • implementation of example of machine learning inference within G4 using external libraries for calorimetry fast simulation
  • Geometrical biaising:
  • Support geometrical biasing
  • Try to merge extended examples with generic biasing where possible/necessary.
  • Generic Biasing:
  • Continue enriching event biasing options:‚Äč
    • DXTRAN-like biasing
    • Implicit capture
    • Occurrence biasing of charged particles, with cross-section changing over the step
    • AMS (Adaptive Multilevel Splitting)
  • Extend generic biasing scheme for at rest case
  • Statistical test suite to verify correctness of biasing wrt to analog
  • Materials:
  • Remove obsolete and improve existing interfaces to materials for the major release
  • Maintenance of basic classes G4Material and associated
  • Improvement of Reverse MC
  • Final Migration and test in MT mode
  • Proton simulation validation
  • Heavy ions.
  • Possible further improvement