DeepEfficiency -在LHC上的高维最优效率反演
DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC
摘要：
我们介绍了一种新的高维算法的效率校正，最大限度地蒙特卡罗事件发生器独立的基准测量在LHC及以后。该方法使用深度神经网络在逐个事件的基础上进行概率驱动，使用检测器模拟甚至仅使用纯相空间分布事件进行训练。这种方法也让我们看到了高能物理学的未来，实验以一种完全多维的方式发布了新型的测量。
英文摘要：
We introduce a new high dimensional algorithm for efficiency corrected, maximally Monte Carlo event generator independent fiducial measurements at the LHC and beyond. The approach is driven probabilistically using a Deep Neural Network on an event-by-event basis, trained using detector simulation and even only pure phase space distributed events. This approach gives also a glimpse into the future of high energy physics, where experiments publish new type of measurements in a radically multidimensional way.
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。
