  Modern machine learning techniques, such as convolutional, recurrent and
recursive neural networks, have shown promise for jet substructure at the Large
Hadron Collider. For example, they have demonstrated effectiveness at boosted
top or W boson identification or for quark/gluon discrimination. We explore
these methods for the purpose of classifying jets according to their electric
charge. We find that both neural networks that incorporate distance within the
jet as an input and boosted decision trees including radial distance
information can provide significant improvement in jet charge extraction over
current methods. Specifically, convolutional, recurrent, and recursive networks
can provide the largest improvement over traditional methods, in part by
effectively utilizing distance within the jet or clustering history. The
advantages of using a fixed-size input representation (as with the CNN) or a
small input representation (as with the RNN) suggest that both convolutional
and recurrent networks will be essential to the future of modern machine
learning at colliders.
