REVIEW 3 cited by
A $W^\pm$ polarization analyzer from Deep Neural Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
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.
Forward citations
Cited by 3 Pith papers
-
Electroweak Symmetry Restoration and Radiation Amplitude Zeros
The paper defines a quantitative measure of electroweak symmetry restoration, δ = M_W/2E, and proposes W±γ, W±Z, and W±H cross-section ratios near radiation amplitude zeros as new high-energy collider observables for ...
-
Optimal sensitivity of anomalous charged triple gauge couplings through $W$ boson helicity at the $e^+e^-$ colliders
SMEFT sensitivities to anomalous WWV couplings at a 3 TeV e+e- collider are improved by 1 to 2 orders of magnitude over LHC limits when using optimal observables and W helicity selection.
-
Transformer networks for Heavy flavor jet tagging
A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.
Discussion (0). Continue with ORCID to comment.