论文标题
通过有条件密度估计，无监督的分布式分布异常检测
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
论文作者
论文摘要
异常检测是机器学习的关键应用，但通常集中在数据的低概率密度区域中偏远样品的检测。相反，我们使用条件密度估计量来提出并激励一种无监督的分布异常检测方法，该估计量旨在找到位于高概率密度区域的独特但完全未知的样品集。作为2020 LHC奥运会盲挑战的一部分，我们将此方法应用于模拟的大型强子对撞机（LHC）粒子碰撞中的新物理学，并显示我们如何在100万碰撞事件中只有0.08％出现的新粒子。我们提出的结果是我们对2020年LHC奥运会的原始盲目提交，在该奥运会上实现了最先进的表现。
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we instead present and motivate a method for unsupervised in-distribution anomaly detection using a conditional density estimator, designed to find unique, yet completely unknown, sets of samples residing in high probability density regions. We apply this method towards the detection of new physics in simulated Large Hadron Collider (LHC) particle collisions as part of the 2020 LHC Olympics blind challenge, and show how we detected a new particle appearing in only 0.08% of 1 million collision events. The results we present are our original blind submission to the 2020 LHC Olympics, where it achieved the state-of-the-art performance.
