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

2020/05/04 by Oliver Knapp, G. Dissertori, Knapp, Oliver +9 · 4 citations
Computer Science · Physics and Astronomy · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2005.01598

openalex publication_date 2020/05/04 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the\nproblem of detecting new physics processes in proton-proton collisions at the\nLarge Hadron Collider. Anomaly detection based on ALAD matches performances\nreached by Variational Autoencoders, with a substantial improvement in some\ncases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show\nhow a data-driven anomaly detection and characterization would work in real\nlife, re-discovering the top quark by identifying the main features of the\nt-tbar experimental signature at the LHC.\n

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