Online-compatible unsupervised nonresonant anomaly detection

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 nonresonant 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 off-line for nonresonant anomaly detection and is also the first complete on-line-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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Recommended citation:

@article{PhysRevD.105.055006,
  title = {Online-compatible unsupervised nonresonant anomaly detection},
  author = {Mikuni, Vinicius and Nachman, Benjamin and Shih, David},
  journal = {Phys. Rev. D},
  volume = {105},
  issue = {5},
  pages = {055006},
  numpages = {9},
  year = {2022},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevD.105.055006},
  url = {https://link.aps.org/doi/10.1103/PhysRevD.105.055006}
}