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arXiv · 2005.01598

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

Oliver Knapp·Guenther Dissertori·Olmo Cerri·Thong Q. Nguyen·Jean-Roch Vlimant·Maurizio Pierini
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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 t-tbar experimental signature at the LHC.

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Oliver Knapp, Guenther Dissertori, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini. 2020-05-04. Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark. https://arxiv.org/abs/2005.01598

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