Apprentice for Event Generator Tuning
Published in 25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021), 2021
Recommended citation: M. Krishnamoorthy, H. Schulz, X. Ju, W. Wang, S. Leyffer, Z. Marshall, S. Mrenna, J. Muller, and J. B.Kowalkowski, Apprentice for Event Generator Tuning. In 25th International Conference on Computing in High-Energy and Nuclear Physics. August 2021. https://doi.org/10.1051/epjconf/202125103060
APPRENTICE is a tool developed for event generator tuning. It contains a range of conceptual improvements and extensions over the tuning tool Professor. Its core functionality remains the construction of a multivariate analytic surrogate model to computationally expensive Monte-Carlo event generator predictions. The surrogate model is used for numerical optimization in chi-square minimization and likelihood evaluation. Apprentice also introduces algorithms to automate the selection of observable weights to minimize the effect of mis-modeling in the event generators. We illustrate our improvements for the task of MC-generator tuning and limit setting.
Recommended citation: M. Krishnamoorthy, H. Schulz, X. Ju, W. Wang, S. Leyffer, Z. Marshall, S. Mrenna, J. Muller, and J. B.Kowalkowski, Apprentice for Event Generator Tuning. In 25th International Conference on Computing in High-Energy and Nuclear Physics. August 2021.