Abstract
We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-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 (CNN), we also explore a composite of two CNNs, 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 signal. The methods presented here can enhance the physics potential in SM measurements and searches for new physics that are sensitive to the electric charge of weak gauge bosons.
Highlights
Boosted heavy resonances play a central role in the study of physics at the Large Hadron Collider (LHC)
By enabling the use of highdimensional, low-level inputs, deep learning automates the process of feature engineering
In addition to a simple convolutional neural networks (CNNs), we will develop a novel composite algorithm consisting of two CNNs, one for each of the Qκ and pT channels, combined in a merge layer, which we refer to as CNN2. This allows us to separately optimize the hyperparameters of the CNNs for the two input channels. We show that this new CNN2 architecture further boosts the performance for most combinations
Summary
Boosted heavy resonances play a central role in the study of physics at the Large Hadron Collider (LHC). Since we are interested in distinguishing Wþ and W− bosons from each other, a key element in our work will be the jet charge observable Qκ [31] incorporated jet charge into various machine learning quark/gluon taggers, including boosted decision trees (BDTs), convolutional neural networks (CNNs), and recurrent neural networks. They showed that including jet charge in the input channels improved quark/gluon discrimination and up vs down quark discrimination. We show results for a binary W−=Wþ classification problem in Sec. IV and compare our performance with the recent work in Ref.
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