Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.

翻译：模拟事件几乎是所有高能物理分析中的关键要素。然而，模拟中的缺陷可能导致观测数据与模拟事件之间存在显著差异。此类模拟失准对相关可观测量造成的影响，必须通过缩放因子、权重或调整观测量分布及其关联性来有效修正。我们提出一种修正方法，通过基于单一正规化流与布尔条件的简洁架构，将多维分布（模拟）转换为另一分布（数据）。我们在具有多个观测量及其关联性非平凡失准的物理启发式模拟数据集上，验证了该方法的有效性。