Boosted 
W
 and 
Z
 tagging with jet charge and deep learning
We demonstrate that the classification of boosted, hadronically decaying, weak gauge bosons can be significantly improved over traditional cut-based and boosted decision tree-based methods using deep learning and the jet charge variable. We construct binary taggers for W+ vs W- and Z vs W discrimination, as well as an overall ternary classifier for W+/W-/Z discrimination. Besides a simple convolutional neural network, we also explore a composite of two simple convolutional neural networks, with different numbers of layers in the jet pT and jet charge channels. We find that this novel structure boosts the performance particularly when considering the Z boson as a signal. The methods presented here can enhance the physics potential in Standard Model measurements and searches for new physics that are sensitive to the electric charge of weak gauge bosons.