Autotunes splits event generator parameter spaces into correlated subspaces and assigns automatic observable weights, enabling iterative Professor-based tuning in higher dimensions than the standard approach.
Tuning of MC generator MPI models
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abstract
MC models of multiple partonic scattering inevitably introduce many free parameters, either fundamental to the models or from their integration with MC treatments of primary-scattering evolution. This non-perturbative and non-factorisable physics in particular cannot currently be constrained from theoretical principles, and hence parameter optimisation against experimental data is required. This process is commonly referred to as MC tuning. We summarise the principles, problems and history of MC tuning, and the still-evolving modern approach to both model optimisation and estimation of modelling uncertainties.
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High dimensional parameter tuning for event generators
Autotunes splits event generator parameter spaces into correlated subspaces and assigns automatic observable weights, enabling iterative Professor-based tuning in higher dimensions than the standard approach.