论文标题
异常意识
Anomaly Awareness
论文作者
论文摘要
我们提出了一种新的用于异常检测的算法，称为异常意识。该算法通过修改成本函数来了解正常事件，同时了解异常。我们展示了该方法如何在不同的粒子物理情况下和标准的计算机视觉任务中起作用。例如，我们将该方法应用于由标准模型顶部和QCD事件产生的脂肪喷射拓扑的图像，并根据一系列新的物理场景进行测试，包括具有EFT效果的HIGGS生产，并共振成两个，三个或四个征片。我们发现该算法可以有效地识别以前从未见过的异常，并且在使其意识到多种多样的异常情况时变得稳健。
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
