Is infrared-collinear safe information all you need for jet classification?

TL;DR

This paper introduces Jet Flow Networks, a new jet classifier that assesses whether infrared-collinear unsafe information enhances discrimination, finding that IRC-safe inputs often suffice for effective jet classification.

Contribution

The paper presents Jet Flow Networks, a permutation-invariant neural network that explores the role of IRC unsafe information in jet classification, demonstrating comparable performance with IRC-safe inputs.

Findings

JFNs perform similarly to Particle Flow Networks with small subjet radii.

Increasing the subjet radius does not significantly degrade performance until physical thresholds are crossed.

JFNs may offer increased model independence at a modest performance cost.

Abstract

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 and a cut on the subjet's transverse momenta serve as tunable hyperparameters enabling a controllable sensitivity to soft emissions and nonperturbative effects. We demonstrate the performance of JFNs for quark vs. gluon and Z vs. QCD jet tagging. For small subjet radii and transverse momentum cuts, 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 on how machines learn patterns in high-energy physics data