Wenjing Wang
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
Authors
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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