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:我们为异常现象的检测提供了一种新的算法,称为异常意识。算法通过修改成本功能来了解正常事件,同时了解不正常现象。我们展示了这种方法在不同粒子物理情况和标准计算机视野任务中是如何运作的。例如,我们将这种方法应用于标准模型顶部和QCD事件产生的肥喷气式地形图象,并对照一系列新的物理假设进行测试,包括带有EFT效应的Higgs生产,以及衰落为2、3或4个子jets的共振现象。我们发现,这种算法有效地识别了以前未曾见过的异常现象,并且变得强大了,因为我们让它意识到了各种各样的异常现象。</s>