Online-compatible Unsupervised Non-resonant Anomaly Detection. (arXiv:2111.06417v1 [cs.LG])

There is a growing need for anomaly detection methods that can broaden the
search for new particles in a model-agnostic manner. Most proposals for new
methods focus exclusively on signal sensitivity. However, it is not enough to
select anomalous events – there must also be a strategy to provide context to
the selected events. We propose the first complete strategy for unsupervised
detection of non-resonant anomalies that includes both signal sensitivity and a
data-driven method for background estimation. Our technique is built out of two
simultaneously-trained autoencoders that are forced to be decorrelated from
each other. This method can be deployed offline for non-resonant anomaly
detection and is also the first complete online-compatible anomaly detection
strategy. We show that our method achieves excellent performance on a variety
of signals prepared for the ADC2021 data challenge.



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