论文标题
有效移动而不是通过机器学习重新持续的对撞机事件
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
论文作者
论文摘要
在撞机物理和其他地方，有很多情况，可以使用校准数据集来预测相位空间目标区域的已知物理和 /或噪声。此校准数据集通常无法开箱即用，但必须经常以有条件的重要性进行调整，以最大程度地现实。以共振异常检测为例，我们比较了基于运输事件的多种替代方法，而不是重新恢复它们。我们发现，变形校准数据集的准确性取决于建立运输任务以进行最佳运输的程度，从而激发了未来的研究。
There are many cases in collider physics and elsewhere where a calibration dataset is used to predict the known physics and / or noise of a target region of phase space. This calibration dataset usually cannot be used out-of-the-box but must be tweaked, often with conditional importance weights, to be maximally realistic. Using resonant anomaly detection as an example, we compare a number of alternative approaches based on transporting events with normalizing flows instead of reweighting them. We find that the accuracy of the morphed calibration dataset depends on the degree to which the transport task is set up to carry out optimal transport, which motivates future research into this area.
