Infrared Safety of a Neural-Net Top Tagging Algorithm

TL;DR

This paper demonstrates that a CNN-based top-jet tagger maintains infrared safety, meaning it is unaffected by soft or collinear gluons, ensuring robustness against modeling uncertainties in jet classification.

Contribution

The paper introduces a CNN-based top-jet tagger that is proven to be infrared safe at the parton level, a property not previously established for such neural network algorithms.

Findings

The CNN tagger's observable is unaffected by soft or collinear gluons.

The CNN tagger is robust against soft and collinear radiation mis-modeling.

The method applies to parton-level boosted top samples.

Abstract

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.