Boosted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>W</mml:mi></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Z</mml:mi></mml:math> tagging with jet charge and deep learning
Yu-Chen Chen, Cheng-Wei Chiang, G. Cottin, David Shih
Abstract
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 ${W}^{\ensuremath{-}}$ and $Z$ vs $W$ discrimination, as well as an overall ternary classifier for ${W}^{+}/{W}^{\ensuremath{-}}/Z$ 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 ${p}_{T}$ 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.