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  • Letter
  • Open Access

Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions

Tanner Mengel, Patrick Steffanic, Charles Hughes, Antonio Carlos Oliveira da Silva, and Christine Nattrass

  • University of Tennessee, Knoxville, Tennessee 37996, USA

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Phys. Rev. C 108, L021901 – Published 22 August, 2023

DOI: https://doi.org/10.1103/PhysRevC.108.L021901

Abstract

Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract background from jets in heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes.

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Phys. Rev. C 108, L021901– Published 22 August, 2023
  • Received 14 March 2023
  • Accepted 9 August 2023
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