High dimensional parameter tuning for event generators

Abstract

AbstractMonte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on precision data is crucial. However, the simultaneous optimization of many parameters is computationally challenging. We present an algorithm that allows to tune Monte Carlo Event Generators for high dimensional parameter spaces. To achieve this we first split the parameter space algorithmically in subspaces and perform a tuning on the subspaces with binwise weights to enhance the influence of relevant observables. We test the algorithm in ideal conditions and in real life examples including tuning of the event generators and for LEP observables. Further, we tune parts of the event generator with the Lund string model.