Deep-learning top taggers or the end of QCD?
- Resource Type
- article
- Authors
- Gregor Kasieczka; Tilman Plehn; Michael Russell; Torben Schell
- Source
- Journal of High Energy Physics, Vol 2017, Iss 5, Pp 1-22 (2017)
- Subject
- Jet substructure
QCD
Hadron-Hadron scattering (experiments)
Top physics
Nuclear and particle physics. Atomic energy. Radioactivity
QC770-798
- Language
- English
- ISSN
- 1029-8479
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.