https://doi.org/10.1140/epjp/s13360-021-01109-4
Regular Article
Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark
1
Institute for Particle Physics and Astrophysics, ETH Zurich, 8093, Zurich, Switzerland
2
Division of Physics, Mathematics and Astronomy, California Institute of Technology, 1200 E. California Blvd, 91125, Pasadena, CA, USA
3
Experimental Physics Department, European Organization for Nuclear Research (CERN), 1211, Geneva, Switzerland
Received:
22
September
2020
Accepted:
11
January
2021
Published online:
19
February
2021
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
of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the 
experimental signature at the LHC.
© The Author(s) 2021
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
