  The application of deep learning techniques using convolutional neural
networks to the classification of particle collisions in High Energy Physics is
explored. An intuitive approach to transform physical variables, like momenta
of particles and jets, into a single image that captures the relevant
information, is proposed. The idea is tested using a well known deep learning
framework on a simulation dataset, including leptonic ttbar events and the
corresponding background at 7 TeV from the CMS experiment at LHC, available as
Open Data. This initial test shows competitive results when compared to more
classical approaches, like those using feedforward neural networks.
