Infrared Safety of a Neural-Net Top Tagging Algorithm
This addresses robustness in boosted top jet tagging for high-energy physics, though it is incremental as it focuses on verifying a known property for a specific method.
The paper tackled the problem of ensuring infrared safety in neural network-based top jet tagging algorithms, demonstrating that a Convolutional Neural Network tagger remains unaffected by soft or collinear gluons in parton-level samples.
Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state. We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collinear with one of the quarks. Our results indicate that the CNN tagger is robust with respect to possible mis-modeling of soft and collinear final-state radiation by Monte Carlo generators.