A W^± polarization analyzer from Deep Neural Networks

2021/02/09 by Taegyun Kim, A. Martin, Kim, Taegyun +1
Physics and Astronomy · #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions #High-Energy Particle Collisions Research

paper · pdf · doi:10.48550/arxiv.2102.05124

Abstract

In this paper, we train a Convolutional Neural Network to classify longitudinally and transversely polarized hadronic W^± using the images of boosted W^± 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^± 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^± 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 → W^±Z events and on pp → W^±Z in the presence of dimension-6 operators that perturb the polarization composition.

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