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BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators

Wang, Wenjing; Krishnamoorthy, Mohan; Muller, Juliane; Mrenna, Stephen; Schulz, Holger; Ju, Xiangyang; Leyffer, Sven; Marshall, Zachary

BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators Thumbnail


Authors

Wenjing Wang

Mohan Krishnamoorthy

Juliane Muller

Stephen Mrenna

Holger Schulz

Xiangyang Ju

Sven Leyffer

Zachary Marshall



Abstract

The parameters in Monte Carlo (MC) event generators are tuned on experimental measurements by evaluating the goodness of fit between the data and the MC predictions. The relative importance of each measurement is adjusted manually in an often time consuming, iterative process to meet different experimental needs. In this work, we introduce several optimization formulations and algorithms with new decision criteria for streamlining and automating this process. These algorithms are designed for two formulations: bilevel optimization and robust optimization. Both formulations are applied to the datasets used in the ATLAS A14 tune and to the dedicated hadronization datasets generated by the SHERPA generator, respectively. The corresponding tuned generator parameters are compared using three metrics. We compare the quality of our automatic tunes to the published ATLAS A14 tune. Moreover, we analyze the impact of a pre-processing step that excludes data that cannot be described by the physics models used in the MC event generators.

Citation

Wang, W., Krishnamoorthy, M., Muller, J., Mrenna, S., Schulz, H., Ju, X., Leyffer, S., & Marshall, Z. (2022). BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators. SciPost Physics Core, 5(1), https://doi.org/10.21468/scipostphyscore.5.1.001

Journal Article Type Article
Acceptance Date Oct 20, 2021
Online Publication Date Jan 17, 2022
Publication Date 2022
Deposit Date Dec 5, 2022
Publicly Available Date Dec 5, 2022
Journal SciPost Physics Core
Publisher SciPost
Peer Reviewed Peer Reviewed
Volume 5
Issue 1
DOI https://doi.org/10.21468/scipostphyscore.5.1.001
Public URL https://durham-repository.worktribe.com/output/1185128

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