Report on 2206.14225v2

Abstract

Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model.The key challenge is that outof-distribution anomaly searches based on the compressibility of features do not apply to the LHC, while existing density-based searches lack performance.We present the first autoencoder which identifies anomalous jets symmetrically in the directions of higher and lower complexity.The normalized autoencoder combines a standard bottleneck architecture with a well-defined probabilistic description.It works better than all available autoencoders for top vs QCD jets and reliably identifies different dark-jet signals.

Authors 5

1. Barry M. Dillon

   Heidelberg University

   Affiliation as printed

   Institut für Theoretische Physik , Universität Heidelberg , Germany

   Institut für Theoretische Physik, Universität Heidelberg, Germany

2. Luigi Favaro

   Heidelberg University

   Affiliation as printed

   Institut für Theoretische Physik , Universität Heidelberg , Germany

   Institut für Theoretische Physik, Universität Heidelberg, Germany

3. Tilman Plehn

   Heidelberg University

   Affiliation as printed

   Institut für Theoretische Physik , Universität Heidelberg , Germany

   Institut für Theoretische Physik, Universität Heidelberg, Germany

4. Peter Sorrenson

   Heidelberg University

   Affiliation as printed

   Heidelberg Collaboratory for Image Processing , Universität Heidelberg , Germany

   Heidelberg Collaboratory for Image Processing, Universität Heidelberg, Germany

5. Michael Krämer Aachen Institute for Theoretical Particle Physics and Cosmology

   RWTH Aachen University

   Affiliation as printed

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

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