Jul 6 – 8, 2021
Europe/Zurich timezone

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

Jul 7, 2021, 11:20 AM


Taegyun Kim (University of Notre Dame)


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 the 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.

Academic Rank PhD student
Affiliation University of Notre Dame

Primary authors

Taegyun Kim (University of Notre Dame) Adam Orion Martin (University of Notre Dame (US))

Presentation materials