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 discrimina...

Full description

Saved in:
Bibliographic Details
Published in:Physical review. D 2020-03, Vol.101 (5), p.1, Article 053001
Main Authors: Chen, Yu-Chen Janice, Chiang, Cheng-Wei, Cottin, Giovanna, Shih, David
Format: Article
Language:English
Subjects:
Artificial neural networks
Bosons
Decision trees
Deep learning
Discrimination
Machine learning
Neural networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2470-0010
2470-0029
DOI:10.1103/PhysRevD.101.053001