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.
The Next Round of Hadronic Generator Tuning Heavily Based on Identified Particle Data
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abstract
Event shape and charged particle inclusive distributions determined from 750 000 hadronic Z events measured with the DELPHI detector at LEP are presented. The statistical and systematic precision of this data allows for a decisive confrontation with Monte Carlo models of the hadronization process and a better understanding of the structure of the Z hadronic final state. Improved tunings of the JETSET, ARIADNE and HERWIG parton shower models and the JETSET matrix element model are obtained by fitting the models to identified particle distributions from all LEP experiments and the DELPHI data presented. The description of the data distributions by the models is critically reviewed with special importance attributed to identified particles.
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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.