Speakers
Description
We discuss recent developments in performance improvements for Monte Carlo integration and event sampling. (1) Massive parallelization of matrix element evaluations based on a new back end for the matrix element generator O'Mega targeting GPUs. This has already been integrated in a development version of the Monte Carlo event generator Whizard for realistic testing and profiling. (2) A complete reconstruction of adaptive multi-channel phase space sampling out of composable parametrized lenses (as defined in category theory and functional programming) for deep learning. We report on two experimental implementations: one in object oriented Fortran, which will be integrated in into Whizard in the future, and another functional programming implementation in ocaml, that is used for cross checks and conceptual developments. Finally, we also report on progress towards a numerically stable inclusion of NLL ISR effects in simulations for future $e^+e^-$ Higgs factories like FCC-ee or ILC/LCF, the extension of methods for fast integrating NLO automated processes to pp colliders and NLO EW corrections, as well as current work on NLO processes in Whizard for EFTs and BSM
models.
Significance
The work on phase space sampling via parametric lenses is a novel development that has not been shown before at any conference. The developments are novel and benchmarks will be created just for ACAT. The GPU development extends previous work on matrix element evaluation on the GPU by now showing phase-space evaluation on the GPU.