Anomaly, or out-of-distribution, detection is a promising tool for aiding
discoveries of new particles or processes in particle physics. In this work, we
identify and address two overlooked opportunities to improve anomaly detection
for high-energy physics. First, rather than train a generative model on the
single most dominant background process, we build detection algorithms using
representation learning from multiple background types, thus taking advantage
of more information to improve estimation of what is relevant for detection.
Second, we generalize decorrelation to the multi-background setting, thus
directly enforcing a more complete definition of robustness for anomaly
detection. We demonstrate the benefit of the proposed robust multi-background
anomaly detection algorithms on a high-dimensional dataset of particle decays
at the Large Hadron Collider.

使用多个背景类型的表示学习构建检测算法以改善异常检测，同时推广多背景设置中的相关性，以更完整地定义鲁棒性，从而在大型强子对撞机的粒子衰变高维数据集上展示了这种算法的好处。