Speaker
Gregor Kasieczka
(Eidgenoessische Technische Hochschule Zuerich (CH))
Description
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 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 show that both approaches lead to comparable performance, establishing convolutional networks as a promising new approach for multivariate hypothesis-based top tagging. Finally, we will comment on new results using machine-learning-based subjet methods.
Primary author
Gregor Kasieczka
(Eidgenoessische Technische Hochschule Zuerich (CH))
