  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.
