  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.
