Apprentice for Event Generator Tuning

18 May 2021, 16:05
Short Talk Offline Computing Algorithms


Mohan Krishnamoorthy (Argonne National Laboratory)


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 mismodeling in the event generators. We illustrate our improvements for the task of MC-generator tuning and limit setting.

Primary authors

Steve Mrenna (Fermi National Accelerator Lab. (US)) Mohan Krishnamoorthy (Argonne National Laboratory) Dr Holger Schulz (University of Durham) James Kowalkowski (Fermi National Accelerator Lab. (US)) Zach Marshall (Lawrence Berkeley National Lab. (US)) Xiangyang Ju (Lawrence Berkeley National Lab. (US)) Sven Leffyer (Argonne National Laboratory) Juliana Mueller (Lawrence Berkeley National Laboratory) Wenjing Wang (Lawrence Berkeley National Laboratory)

Presentation materials