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

doi:10.21468/SciPostPhysCore.5.1.001

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

Journal Article · 16 January 2022 · SciPost Physics Core

DOI: https://doi.org/10.21468/SciPostPhysCore.5.1.001 · OSTI ID:1840611

Wang, Wenjing ^[1]; Krishnamoorthy, Mohan ^[2]; Muller, Juliane ^[1]; Mrenna, Stephen ^[3]; Schulz, Holger ^[4]; Ju, Xiangyang ^[1]; Leyffer, Sven ^[2]; Marshall, Zachary ^[1]

Lawrence Berkeley National Laboratory
Argonne National Laboratory
Fermi National Accelerator Laboratory
Durham University

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231; AC02-06CH11357; AC02-07CH11359; FG02-05ER25694

OSTI ID:: 1840611

Journal Information:: SciPost Physics Core, Journal Name: SciPost Physics Core Journal Issue: 1 Vol. 5; ISSN 2666-9366

Publisher:: Stichting SciPostCopyright Statement

Country of Publication:: Netherlands

Language:: English

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