A

What's anomalous in LHC jets?

SciPost Physics, vol. 15

Abstract

Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers. We discuss specific examples how LHC anomalies can be defined through probability density estimates, evaluated in a physics space or in an appropriate neural network latent space, and discuss the model-dependence in choosing an appropriate data parameterisation. We illustrate this for classical k-means clustering, a Dirichlet variational autoencoder, and invertible neural networks. For two especially challenging scenarios of jets from a dark sector we evaluate the strengths and limitations of each method.

Authors 8

  1. Heidelberg University

    Affiliation as printed

    Heidelberg University

  2. Heidelberg University

    Affiliation as printed

    Heidelberg University

  3. RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University

    Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany

  4. RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University

    Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany

  5. RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University

    Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany

  6. RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University

    Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany

  7. RWTH Aachen University

    Affiliation as printed

    RWTH Aachen University

    Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, Germany

  8. Heidelberg University

    Affiliation as printed

    Heidelberg University

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References 122