论文标题
在CMS开放数据上对抗学到的异常检测：重新发现顶级夸克
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
论文作者
论文摘要
我们在大型强生对撞机的质子 - 普罗顿碰撞中检测新物理过程的问题应用了对抗性学习的异常检测（ALAD）算法。基于ALAD的异常检测与变异自动编码器达到的性能相匹配，在某些情况下会有了很大的改善。在8个TEV CMS开放数据的4.4 fb-1上训练Alad算法，我们展示了数据驱动的异常检测和表征如何在现实生活中起作用，通过识别LHC的T-TBAR实验签名的主要特征来重新发现顶级夸克。
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the t-tbar experimental signature at the LHC.
