我的死亡报告被大大夸大：$N$ -subjettiness标签采取喷气机图像
Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images
摘要：
我们比较了在喷流图像上训练的卷积神经网络（CNN）与在n-subjettiness变量上训练的密集神经网络（DNN）的性能，以研究这两种单独技术应用于顶夸克衰变的区分能力。我们发现，它们的表现几乎相同，并高度相关，一旦喷流质量信息，这表明他们正在访问相同的基本信息，可以直观地理解为包含在4-，5-，6-和8-体运动学相空间取决于样本。这表明这两种方法对于重物标记都非常有用，并为图像网络实际学习的问题提供了一个初步的答案。
英文摘要：
We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distinguishing power of these two separate techniques applied to top quark decays. We find that they perform almost identically and are highly correlated once jet mass information is included, which suggests they are accessing the same underlying information which can be intuitively understood as being contained in 4-, 5-, 6-, and 8-body kinematic phase spaces depending on the sample. This suggests both of these methods are highly useful for heavy object tagging and provides a tentative answer to the question of what the image network is actually learning.
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。
