Automatic Monte-Carlo Tuning for Minimum Bias Events at the LHC

The Large Hadron Collider near Geneva Switzerland will ultimately collide protons at a center-of-mass energy of $14\tev$ and $40\mhz$ bunch crossing rate with a luminosity of $\lumi{10^{34}}$. At each bunch crossing about 20 soft proton-proton interactions are expected to happen. In order to study new phenomena and improve our current knowledge of the physics these events must be understood. However, the physics of soft interactions are not completely known at such high energies. Different phenomenological models, trying to explain these interactions, are implemented in several Monte-Carlo (MC) programs such as PYTHIA, PHOJET and EPOS. Some parameters in such MC programs can be tuned to improve the agreement with the data. In this thesis a new method for tuning the MC programs, based on Genetic Algorithms and distributed analysis techniques have been presented. This method represents the first and fully automated MC tuning technique that is based on true MC distributions. It is an alternative to parametrization-based automatic tuning. This new method is used in finding new tunes for PYTHIA 6 and 8. These tunes are compared to the tunes found by alternative methods, such as the PROFESSOR framework and manual tuning, and found to be equivalent or better. Charged particle multiplicity, $d\nch/d\eta$, Lorentz-invariant yield, transverse momentum and mean transverse momentum distributions at various center-of-mass energies are generated using default tunes of EPOS, PHOJET and the Genetic Algorithm tunes of PYTHIA 6 and 8. These distributions are compared to measurements from UA5, CDF, CMS and ATLAS in order to investigate the best model available. Their predictions for the ATLAS detector at LHC energies have been investigated both with generator level and full detector simulation studies.