Event-based anomaly detection for new physics searches at the LHC using machine learning

ORAL

Abstract

This paper discusses model-agnostic searches for new physics at the Large Hadron Collider (LHC) using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly detection in the context of machine-learning approaches using autoencoders, and illustrate expected shapes of invariant masses in the outlier region using Monte Carlo simulations. Challenges and conceptual limitations of this approach are discussed.

*We acknowledge the computing resources provided by the Laboratory Computing Resource Center at Argonne National Laboratory. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with theDOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan. Argonne National Laboratory’s work was funded by the U.S. Department of Energy, Office of High Energy Physics under c

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Publication: S.V.Chekanov, W.Hopkins, Event-based anomaly detection for new physics searches at the LHC using machine learning, https://arxiv.org/abs/2111.12119, ANL-HEP-17239 (2020)

Presenters

  • Sergei Chekanov

    • Argonne National Laboratory

Authors

  • Sergei Chekanov

    • Argonne National Laboratory

  • Walter Hopkins

    • Argonne National Laboratory