SciPost Submission Page
BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators
by Wenjing Wang, Mohan Krishnamoorthy, Juliane Muller, Stephen Mrenna, Holger Schulz, Xiangyang Ju, Sven Leyffer, Zachary Marshall
This Submission thread is now published as
Submission summary
| Submission information |
| Preprint Link: |
scipost_202103_00005v3
(pdf)
|
| Date accepted: |
2021-10-20 |
| Date submitted: |
2021-09-01 23:47 |
| Submitted by: |
Wang, Wenjing |
| Submitted to: |
SciPost Physics |
| Ontological classification |
| Academic field: |
Physics |
| Specialties: |
- High-Energy Physics - Experiment
|
| Approaches: |
Experimental, Computational |
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.
Author: Wenjing Wang on 2021-09-30 [id 1791]
(in reply to Report 1 on 2021-09-27)Thank you for your positive comments! We appreciate your support of our paper.