Jet Charge and Machine Learning
This work addresses jet charge classification for particle physics experiments, representing an incremental application of existing machine learning techniques to a specific domain.
The paper tackled the problem of classifying jets by electric charge at the Large Hadron Collider using machine learning, finding that neural networks incorporating distance information and boosted decision trees significantly improve jet charge extraction over traditional methods, with convolutional, recurrent, and recursive networks showing the largest gains.
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.