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

Submission summary

As Contributors: Wenjing Wang
Preprint link: scipost_202103_00005v3
Date submitted: 2021-09-01 23:47
Submitted by: Wang, Wenjing
Submitted to: SciPost Physics
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.

Current status:
Editor-in-charge assigned



Reports on this Submission

Anonymous Report 1 on 2021-9-27 (Invited Report)

Report

The new version clarified all my remaining questions. I agree with the paper being accepted for publication

  • validity: high
  • significance: good
  • originality: good
  • clarity: good
  • formatting: good
  • grammar: good

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.

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