A $W^{\pm }$ polarization analyzer from Deep Neural Networks

Abstract

In this paper, we train a Convolutional Neural Network to classify longitudinally and transversely polarized hadronic $W^{\pm }$ using the images of boosted $W^{\pm }$ jets as input. The images capture angular and energy information from the jet constituents that is faithful to properties of the original quark/anti-quark $W^{\pm }$ decay products without the need for invasive substructure cuts. We find that the difference between the polarizations is too subtle for the network to be used as an event-by-event tagger. However, given an ensemble of $W^{\pm }$ events with unknown polarization, the average network output from that ensemble can be used to extract the longitudinal fraction $f_L$. We test the network on Standard Model $pp\to W^{\pm }Z$ events and on $pp\to W^{\pm }Z$ in the presence of dimension-6 operators that perturb the polarization composition.

References in corpus (9)

• PYTHIA 6.4 Physics and Manual
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Jet substructure as a new Higgs search channel at the LHC
• Top-tagging: A Method for Identifying Boosted Hadronic Tops
• Techniques for improved heavy particle searches with jet substructure
• Strategies to Identify Boosted Tops
• Measurement of the W boson polarization in top quark decays with the ATLAS detector
• O(α_s) corrections to the decays of polarized W^+- and Z bosons into massive quark pairs

Cited by in corpus (2)

• Measurement of Higgs boson self-couplings through $2\to 3$ vector bosons scattering in future muon colliders
• Electroweak Restoration at the LHC and Beyond: The $Vh$ Channel