Adversarially Learned Anomaly Detection on CMS open data: re-discovering the top quark

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Author
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Date
2021Type
- Journal Article
Citations
Cited 26 times in
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Cited 27 times in
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ETH Bibliography
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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 tt¯ experimental signature at the LHC. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000472827Publication status
publishedExternal links
Journal / series
The European Physical Journal PlusVolume
Pages / Article No.
Publisher
SpringerOrganisational unit
03593 - Dissertori, Günther / Dissertori, Günther
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Citations
Cited 26 times in
Web of Science
Cited 27 times in
Scopus
ETH Bibliography
yes
Altmetrics