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

  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},
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… 

Figures from this paper

Polarization fraction measurement in same-sign WW scattering using deep learning

Studying the longitudinally polarized fraction of $W^\pm W^\pm$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and

Polarization measurement for the dileptonic channel of W+W− scattering using generative adversarial network

1 College of Physics, Sichuan University, Chengdu 610065, China Abstract Measuring polarization modes of the W+W− scattering reveals the interactions of the Higgs boson as well as new neutral states

Sensitivity to longitudinal vector boson scattering in semileptonic final states at the HL-LHC

Longitudinal vector boson scattering provides an important probe of electroweak symmetry breaking, bringing sensitivity to physics beyond the Standard Model as well as constraining properties of the

Detecting anomaly in vector boson scattering

A neural network is proposed which compress the features of the vector boson scattering into three dimensional latent space and is capable of distinguish different polarization modes of $WWjj$ production in both dileptonic channel and semi-leptonic channels.

Deep-Learning in Search of Light Charged Higgs

This work construct Deep Neural Networks with appropriate architecture and determine signal extraction efficiency by considering various features (kinematical and human engineered parameters), showing that DNN gives better performance than the classical neural networks and has ability to find regions of high efficiency even the input features are not human-engineered.

Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning

High-level features of pion spectra captured by a carefully-trained neural network were found to be able to distinguish the nature of the QCD transition even in a simulation scenario which is close to the experiments.

Quark jet versus gluon jet: fully-connected neural networks with high-level features

Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images

NLO QCD corrections to polarised di-boson production in semi-leptonic final states

: Understanding the polarisation structure and providing precise predictions for multi-boson processes at the LHC is becoming urgent in the light of the upcoming run-3 and high-luminosity data. The

Class imbalance techniques for high energy physics

A common problem in a high energy physics experiment is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples



Nature Commun

  • 5
  • 2014

Mathematics for Control

Acta Phys

  • Polon. B8
  • 1977


  • Phys. Commun. 178
  • 2008

and Y

  • Bengio, CoRR abs/1211.5590
  • 2012