Abstract
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.
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