  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.
