Anomaly Awareness
This work addresses anomaly detection for particle physics and computer vision applications, presenting an incremental improvement through a modified cost function approach.
The researchers tackled anomaly detection by developing the Anomaly Awareness algorithm, which modifies the cost function to learn normal events while being aware of anomalies, and demonstrated its effectiveness in identifying previously unseen anomalies in particle physics and computer vision tasks, achieving robustness with a varied anomaly set.
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.