Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark

@article{Knapp2021AdversariallyLA,
  title={Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark},
  author={Oliver Knapp and Guenther Dissertori and Olmo Cerri and Thong Q. Nguyen and J. R. Vlimant and Maurizio Pierini},
  journal={ArXiv},
  year={2021},
  volume={abs/2005.01598}
}
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton–proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb$$^{-1}$$ - 1 of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re… 
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