Boosted W/Z Tagging with Jet Charge and Deep Learning
Boosted W=Z Tagging with Jet Charge and Deep Learning Yu-Chen Janice Chen1, Cheng-Wei Chiang1;2;3, Giovanna Cottin1;4;5 and David Shih6 1 Department of Physics, National Taiwan University, Taipei 10617, Taiwan 2 Institute of Physics, Academia Sinica, Taipei 11529, Taiwan 3 Department of Physics and Center of High Energy and High Field Physics, National Central University, Chungli 32001, Taiwan 4 Instituto de F´ısica, Pontificia Universidad Cat´olica de Chile, Avenida Vicu~na Mackenna 4860, Santiago, Chile 5 Departamento de Ciencias, Facultad de Artes Liberales, Universidad Adolfo Ib´a~nez, Diagonal Las Torres 2640, Santiago, Chile 6 NHETC, Dept. of Physics and Astronomy, Rutgers, The State University of NJ Piscataway, NJ 08854, USA 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 arXiv:1908.08256v2 [hep-ph] 19 Mar 2020 neural network (CNN), we also explore a composite of two CNNs, 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 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. 1 Introduction Boosted heavy resonances play a central role in the study of physics at the Large Hadron Collider (LHC).
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