Apprentice for Event Generator Tuning

TL;DR

Apprentice is a novel tool that enhances event generator tuning by using surrogate models and automated observable weight selection, improving efficiency and accuracy in Monte Carlo simulations.

Contribution

It introduces conceptual improvements and algorithms for automating observable weight selection, advancing the state-of-the-art in event generator tuning.

Findings

Improved tuning accuracy demonstrated on MC-generator tasks.

Automated weight selection reduces mis-modeling effects.

Surrogate models enable efficient optimization.

Abstract

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.