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

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

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

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.

Received 14 March 2023
Accepted 9 August 2023

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

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP³.

Published by the American Physical Society