# Determination of the WW polarization fractions in pp→W±W±jj using a deep machine learning technique

@article{Searcy2015DeterminationOT,
title={Determination of the WW polarization fractions in pp→W±W±jj using a deep machine learning technique},
author={Jacob Searcy and Lillian Huang and Marc-Andr{\'e} Pleier and Junjie Zhu},
journal={Physical Review D},
year={2015},
volume={93},
pages={094033}
}
• Published 6 October 2015
• Physics
• Physical Review D
The unitarization of the longitudinal vector boson scattering (VBS) cross section by the Higgs boson is a fundamental prediction of the Standard Model which has not been experimentally verified. One of the most promising ways to measure VBS uses events containing two leptonically-decaying same-electric-charge $W$ bosons produced in association with two jets. However, the angular distributions of the leptons in the $W$ boson rest frame, which are commonly used to fit polarization fractions, are…
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## References

SHOWING 1-5 OF 5 REFERENCES

• 5
• 2014

• Polon. B8
• 1977

### Comput

• Phys. Commun. 178
• 2008

### and Y

• Bengio, CoRR abs/1211.5590
• 2012