Jet Charge and Machine Learning
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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 neural networks that incorporate distance within the jet as an input can provide significant improvement in jet charge extraction over traditional methods. We find that both convolutional and recurrent networks are effective and both train faster than recursive networks. The advantages of using a fixed-size input representation (as with the CNN) or a smaller 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.
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