Tag Z boson jets via convolutional neural networks

Abstract

The jet tagging task in high-energy physics is to distinguish signals of interest from the background, which is of great importance for the discovery of new particles, or new processes, at the large hadron collider. The energy deposition generated in the calorimeter can be seen as a kind of picture. Based on this notion, tagging jets initiated by different processes becomes a classic image classification task in the computer vision field. We use jet images as the input built on high dimensional low-level information, energy-momentum four-vectors, to explore the potential of convolutional neural networks (CNNs). Four models of different depths are designed to make the best underlying useful features of jet images. Traditional multivariable method, boosted decision tree (BDT), is used as a baseline to determine the performance of networks. We introduce four observable quantities into BDTs: the mass, transverse momenta of fat jets, the distance between the leading and subleading jets, and N-subjettiness. Different tree numbers are adopted to build three kinds of BDTs, which is intended to have variable classifying abilities. After training and testing, the results show that the CNN 3 is the neatest and most efficient network under the design of stacking convolutional layers. Deepening the model could improve the performance to a certain extent but it is unable to work all the time. The performances of all BDTs are almost the same, which is possibly due to a small number of input observable types. The performance metrics show that the CNNs outperform the BDTs: the background rejection efficiency increases up to 150% at 50% signal efficiency. Besides, after inspecting the best and the worst samples, we conclude the characteristics of jets initiated by different processes: jets obtained by Z boson decays tend to concentrate in the center of jet images or have a clear differentiable substructure; the substructures of jets from general quantum chromodynamics processes have more random forms and not only just have two subjets. As the final step, the confusion matrix of the CNN 3 indicate that it comes to be kind of conservative. Exploring the way of keeping the balance between conservative and radical is our goal in the future work.