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.

翻译：我们提出一种名为“异常感知”的新型异常检测算法。该算法通过学习正常事件，同时通过修改代价函数使其感知异常。我们展示了该方法在不同粒子物理场景和标准计算机视觉任务中的工作原理。例如，我们将该方法应用于标准模型顶夸克和量子色动力学事件生成的胖喷流拓扑图像，并针对一系列新物理场景进行测试，包括具有有效场论效应的希格斯产生以及衰变为两个、三个或四个子喷流的共振态。我们发现该算法能有效识别未见过的异常，并随着其感知多样化的异常集而变得稳健。