Quark-Gluon Jet Discrimination Using Convolutional Neural Networks

Understand

Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data.

• One such challenging task is to distinguish quark-initiated jets from gluon-initiated jets.
• Following previous work, we treat the jet as an image by pixelizing track information and calorimeter deposits as reconstructed by the detector.
• We test the deep learning paradigm by training several recently developed, state-of-the-art convolutional neural networks on the quark-gluon discrimination task.