  Machine learning-based jet classifiers are able to achieve impressive tagging
performance in a variety of applications in high energy and nuclear physics.
However, it remains unclear in many cases which aspects of jets give rise to
this discriminating power, and whether jet observables that are tractable in
perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as
sufficient inputs. In this article, we introduce a new classifier, Jet Flow
Networks (JFNs), in an effort to address the question of whether IRC unsafe
information provides additional discriminating power in jet classification.
JFNs are permutation-invariant neural networks (deep sets) that take as input
the kinematic information of reconstructed subjets. The subjet radius serves as
a tunable hyperparameter, enabling the sensitivity to soft emissions and
nonperturbative effects to be gradually increased as the subjet radius is
decreased. We demonstrate the performance of JFNs for quark vs. gluon and QCD
vs. $Z$ jet tagging. For small subjet radii, the performance of JFNs is
equivalent to the IRC-unsafe Particle Flow Networks (PFNs), demonstrating that
infrared-collinear unsafe information is not necessary to achieve strong
discrimination for both cases. As the subjet radius is increased, the
performance of the JFNs remains essentially unchanged until physical thresholds
that we identify are crossed. For relatively large subjet radii, we show that
the JFNs may offer an increased model independence with a modest tradeoff in
performance compared to classifiers that use the full particle information of
the jet. These results shed new light onto how machines learn patterns in
high-energy physics data.
