Apprentice for Event Generator Tuning

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.

arXiv comment9 pages, 2 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physics

References in corpus (6)

• MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics
• X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue
• Systematic event generator tuning for the LHC
• Analysis strategies for general spin-independent WIMP-nucleus scattering
• Superplot: a graphical interface for plotting and analysing MultiNest output
• Surrogate Models for Direct Dark Matter Detection

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