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.
@article{arxiv.2103.05748,
title = {Apprentice for Event Generator Tuning},
author = {Mohan Krishnamoorthy and Holger Schulz and Xiangyang Ju and Wenjing Wang and Sven Leyffer and Zachary Marshall and Stephen Mrenna and Juliane Muller and James B. Kowalkowski},
journal= {arXiv preprint arXiv:2103.05748},
year = {2021}
}
Comments
9 pages, 2 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physics