Tuning of the PYTHIA 6.4 Multiple Parton Interaction model to Minimum Bias and Underlying Event data

QCD has been quite successful in describing hadronic interactions at large transfer momenta, also known as hard interactions. However high energy pp and p p collisions are dominated by soft partonic collisions. Di erent phenomenological models are implemented in several Monte Carlo (MC) event generators such as PYTHIA, PHOJET and HERWIG etc., which attempt to simulate these interactions. These MC event generators have free parameters which need to be tuned to improve the agreement with the data. In this thesis the MC event generator PYTHIA6.424 is considered and the optimization of its model parameters have been presented. This work mainly focuses on tuning of multiple parton interaction parameters to Minimum Bias and Underlying event published data from ATLAS at 0.9 and 7TeV and from CDF II at 1.96 TeV. The method employed to tune the parameters is based on a linear and iterative approach and allows the simultaneous variation of many parameters. Six parameters are tuned, which are found to be very sensitive to the chosen data distributions using nine PDF sets of three di erent types: Leading order, MC adapted and Next-to-leading order. A signi cant connection is observed between the PDF being used and the parameter values obtained. The overall t quality is reasonably good for all the PDF sets.