  Multivariate techniques based on engineered features have found wide adoption
in the identification of jets resulting from hadronic top decays at the Large
Hadron Collider (LHC). Recent Deep Learning developments in this area include
the treatment of the calorimeter activation as an image or supplying a list of
jet constituent momenta to a fully connected network. This latter approach
lends itself well to the use of Recurrent Neural Networks. In this work the
applicability of architectures incorporating Long Short-Term Memory (LSTM)
networks is explored. Several network architectures, methods of ordering of jet
constituents, and input pre-processing are studied. The best performing LSTM
network achieves a background rejection of 100 for 50% signal efficiency. This
represents more than a factor of two improvement over a fully connected Deep
Neural Network (DNN) trained on similar types of inputs.
