用机器学习有效移动而不是重新加权碰撞器事件
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
摘要：
在对撞机物理学和其他地方存在许多情况，其中校准数据集用于预测相空间的目标区域的已知物理和/或噪声。该校准数据集通常不能开箱即用，但必须进行调整（通常使用条件重要性权重），以最大限度地真实。以共振异常检测为例，我们比较了一些替代方法的基础上传输事件与正常化流，而不是重新加权。我们发现，变形校准数据集的准确性取决于运输任务的设置程度，以进行最佳运输，这激励了未来的研究进入这一领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
