Boosting mono-jet searches with model-agnostic machine learning

Journal of High Energy Physics, vol. 2022

Abstract

Abstract We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without Labels (CWoLa) method is used to extract all the information available from low-level detector information without any reference to specific new physics models. For the example of a strongly interacting dark matter model, we employ simulated data to show that the discovery potential of an existing generic search can be boosted considerably.

Authors 4

1. Thorben Finke corresponding 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, D-52056, Aachen, Germany

2. M. 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, D-52056, Aachen, Germany

3. Maximilian Lipp 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, D-52056, Aachen, Germany

4. Alexander Mück 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, D-52056, Aachen, Germany