2018/06/30 by Suyong Choi, Seung J. Lee, Maxim Perelstein · 1 citation
Computer Science · Physics and Astronomy · #Convolutional neural network #Gluon #High-Energy Particle Collisions Research #Infrared #Jet (fluid) #Monte Carlo method #Observable #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions #cs.CV #hep-ph
paper · pdf · doi:10.1007/jhep02(2019)132
7 pages, 8 figures, final version to be published in JHEP
openalex created_date 2018/06/13 · openalex publication_date 2019/02/01 · arxiv created 2019/02/16 · arxiv updated 2019/03/27 · openalex updated_date 2026/08/06
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.