Published February 2021
| Accepted Version + Published
Journal Article
Open
Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark
Abstract
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 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 tt̄ experimental signature at the LHC.
Additional Information
© The Author(s) 2021. 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/. Received: 22 September 2020 / Accepted: 11 January 2021. This work was possible thanks to the commitment of the CMS collaboration to release its data and MC samples through the CERN Open Data portal. We would like to thank our CMS colleagues and the CERN Open Data team for their effort to promote open access to science. In particular, we thank Kati Lassila-Perini for her precious help. This project is partially supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No. 772369) and by the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925. This work was conducted at "iBanks," the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of "iBanks." Open Access funding provided by CERN. Data Availability Statement: This manuscript has associated data in a data repository. [Authors' comment: the Delphes datasets is released on Zenodo and available at https://zenodo.org/communities/mpp-hep. The CMS Open Data are released on the CERN Open Data portal at https://opendata.cern.ch/search?experiment=CMS.]Attached Files
Published - Knapp2021_Article_AdversariallyLearnedAnomalyDet.pdf
Accepted Version - 2005.01598.pdf
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Knapp2021_Article_AdversariallyLearnedAnomalyDet.pdf
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Additional details
- Eprint ID
- 108139
- Resolver ID
- CaltechAUTHORS:20210222-100903670
- 772369
- European Research Council (ERC)
- DE-SC0011925
- Department of Energy (DOE)
- NVIDIA Corporation
- SuperMicro Corporation
- Kavli Foundation
- CERN
- Created
-
2021-02-23Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- CMS@Caltech