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.
Highlights
Our goal is to find a set of physics parameters, p∗, that minimizes the difference between the experimental data and the simulated data from an Monte Carlo (MC) event generator
We propose several algorithms for automating the weighting the importance of data used in the tuning process for Monte Carlo event generators
The second used data and predictions are from the Large Electron-Positron (LEP) Collider and had only the default parameter choices as a reference
Summary
Our goal is to find a set of physics parameters, p∗, that minimizes the difference between the experimental data and the simulated data from an MC event generator. Our goal is to automate the weight adjustment, yielding a less subjective and less time-consuming process to find the optimal physics parameters p that will be used in the actual MC simulation. Instead of minimizing only an expected value and potentially obtaining a solution that allows for some observables having large errors and others small errors, we aim to find a solution that provides a good tradeoff between both metrics
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