Online-compatible unsupervised nonresonant anomaly detection
V. M. Mikuni, Benjamin Philip Nachman, David Shih · Physical review. D/Physical review. D. · 2022
The authors of this paper employ two (or more) autoencoders to provide a complete strategy for unsupervised non-resonant anomaly detection. Both signal extraction and data-driven background estimation can be determined with decorrelated autoencoders. The method shows strong performance on test datasets and has the advantage of being online-compatible.