Deep-learning top taggers or the end of QCD?

Kasieczka, G., Plehn, T., Russell, M. and Schell, T. (2017) Deep-learning top taggers or the end of QCD? Journal of High Energy Physics, 2017(5), 6. (doi: 10.1007/JHEP05(2017)006)

Abstract

Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. We first optimize a network architecture to identify top quarks in Monte Carlo simulations of the Standard Model production channel. Using standard fat jets we then compare its performance to a multivariate QCD-based top tagger. We find that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Russell, Mr Michael
• Authors: Kasieczka, G., Plehn, T., Russell, M., and Schell, T.
• College/School: College of Science and Engineering > School of Physics and Astronomy
• Journal Name: Journal of High Energy Physics
• Publisher: Springer
• ISSN: 1029-8479
• ISSN (Online): 1029-8479
• Published Online: 02 May 2017
• Copyright Holders: Copyright © 2017 The Authors
• First Published: First published in Journal of High Energy Physics 2017(5): 6
• Publisher Policy: Reproduced under a Creative Commons License