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.
翻译:Monte Carlo(MC)事件生成器的参数通过评价数据和MC预测之间的适当性来根据实验性测量来调整蒙特卡洛事件生成器的参数。每种测量的相对重要性在通常耗时、迭代的过程中手工调整,以满足不同的实验需要。在这项工作中,我们引入了几种优化配方和算法,并提出了简化和自动化这一过程的新决定标准。这些算法是为两种配方设计的:双级优化和强力优化。两种配方分别适用于ATLAS A14调中使用的数据集和Sherpa发电机生成的专用超时化数据集。相应的调动发电机参数用三个尺度进行比较。我们比较了我们的自动调子的质量与公布的ATLAS A14调。此外,我们分析了预先处理步骤的影响,该步骤排除了在MC事件生成器中使用的物理模型无法描述的数据。