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arXiv · 2303.08275

Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions

Tanner Mengel·Patrick Steffanic·Charles Hughes·Antonio Carlos Oliveira da Silva·Christine Nattrass
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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 the background for measurements of jets in relativistic 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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Tanner Mengel, Patrick Steffanic, Charles Hughes, Antonio Carlos Oliveira da Silva, Christine Nattrass. 2023-03-14. Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions. https://doi.org/10.1103/physrevc.108.l021901

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