  Building on the notion of a particle physics detector as a camera and the
collimated streams of high energy particles, or jets, it measures as an image,
we investigate the potential of machine learning techniques based on deep
learning architectures to identify highly boosted W bosons. Modern deep
learning algorithms trained on jet images can out-perform standard
physically-motivated feature driven approaches to jet tagging. We develop
techniques for visualizing how these features are learned by the network and
what additional information is used to improve performance. This interplay
between physically-motivated feature driven tools and supervised learning
algorithms is general and can be used to significantly increase the sensitivity
to discover new particles and new forces, and gain a deeper understanding of
the physics within jets.
