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:​
  • 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