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  • Letter
  • Open Access

Deep-learned event variables for collider phenomenology

Doojin Kim1,*, Kyoungchul Kong2,†, Konstantin T. Matchev3,‡, Myeonghun Park4,5,6,§, and Prasanth Shyamsundar7,∥

  • 1Mitchell Institute for Fundamental Physics and Astronomy, Department of Physics and Astronomy, Texas A&M University, College Station, Texas 77843, USA
  • 2Department of Physics and Astronomy, University of Kansas, Lawrence, Kansas 66045, USA
  • 3Institute for Fundamental Theory, Physics Department, University of Florida, Gainesville, Florida 32611, USA
  • 4Institute of Convergence Fundamental Studies, Seoultech, Seoul 01811, Korea
  • 5School of Physics, KIAS, Seoul 02455, Korea
  • 6Center for Theoretical Physics of the Universe, Institute for Basic Science, Daejeon 34126 Korea
  • 7Fermilab Quantum Institute, Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA

  • *doojin.kim@tamu.edu
  • †kckong@ku.edu
  • ‡matchev@ufl.edu
  • §parc.seoultech@seoultech.ac.kr
  • ∥prasanth@fnal.gov
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Phys. Rev. D 107, L031904 – Published 28 February, 2023

DOI: https://doi.org/10.1103/PhysRevD.107.L031904

Abstract

The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for many typical event topologies in collider physics. Here, we introduce a deep-learning technique to design good event variables, which are sensitive over a wide range of values for the unknown model parameters. We demonstrate that the neural networks trained with our technique on some simple event topologies are able to reproduce standard event variables like invariant mass, transverse mass, and stransverse mass. The method is automatable and completely general and can be used to derive sensitive, previously unknown, event variables for other, more complex event topologies.

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Phys. Rev. D 107, L031904– Published 28 February, 2023
  • Received 2 June 2021
  • Accepted 12 February 2023
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