Infrared safety of a neural-net top tagging algorithm

• Published in: The journal of high energy physics Vol. 2019; no. 2; pp. 1 - 14
• Main Authors: Choi, Suyong, Lee, Seung J., Perelstein, Maxim
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2019; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial neural networks; Classical and Quantum Gravitation; Computer simulation; Elementary Particles; High energy physics; Jets; Marking; Neural networks; Physics; Physics and Astronomy; QCD Phenomenology; Quantum Field Theories; Quantum Field Theory; Quantum Physics; Regular Article - Theoretical Physics; Relativity Theory; Safety; String Theory; Jets; QCD Phenomenology
• ISSN: 1029-8479; 1126-6708; 1127-2236; 1029-8479
• DOI: 10.1007/JHEP02(2019)132

A bstract 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.