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arXiv · 2103.05748

Apprentice for Event Generator Tuning

Mohan Krishnamoorthy·Holger Schulz·Xiangyang Ju·Wenjing Wang·Sven Leyffer·Zachary Marshall·Stephen Mrenna·Juliane Muller·James B. Kowalkowski
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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.

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Mohan Krishnamoorthy, Holger Schulz, Xiangyang Ju, Wenjing Wang, Sven Leyffer, Zachary Marshall, Stephen Mrenna, Juliane Muller, James B. Kowalkowski. 2021-03-09. Apprentice for Event Generator Tuning. https://doi.org/10.1051/epjconf/202125103060

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