Apprentice for Event Generator Tuning
- Resource Type
- Authors
- Stephen Mrenna; Wenjing Wang; James B. Kowalkowski; Holger Schulz; Mohan Krishnamoorthy; Xiangyang Ju; Juliane Müller; Z. Marshall; Sven Leyffer
- Source
- EPJ Web of Conferences, Vol 251, p 03060 (2021)
- Subject
- Computer science
Event (computing)
Physics
QC1-999
FOS: Physical sciences
Control engineering
Computational Physics (physics.comp-ph)
High Energy Physics - Experiment
Task (project management)
High Energy Physics - Phenomenology
Range (mathematics)
High Energy Physics - Experiment (hep-ex)
Surrogate model
High Energy Physics - Phenomenology (hep-ph)
Limit setting
Minification
Physics - Computational Physics
Selection (genetic algorithm)
Event generator
- Language
- English
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.
Comment: 9 pages, 2 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physics