Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.
A new method to distinguish hadronically decaying boosted $Z$ bosons from $W$ bosons using the ATLAS detector
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The distribution of particles inside hadronic jets produced in the decay of boosted $W$ and $Z$ bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted $W$ or $Z$ boson, this paper presents a technique for further differentiating $Z$ bosons from $W$ bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of $W'\rightarrow WZ$ for bosons in the transverse momentum range 200 GeV $<p_{T}<$ 400 GeV in $\sqrt{s}=8$ TeV $pp$ collisions with the ATLAS detector at the LHC. For $Z$-boson tagging efficiencies of $\epsilon_Z=$ 90%, 50%, and 10%, one can achieve $W^+$-boson tagging rejection factors ($1/\epsilon_{W^+}$) of 1.7, 8.3 and 1000, respectively. It is not possible to measure these efficiencies in the data due to the lack of a pure sample of high $p_{T}$, hadronically decaying $Z$ bosons. However, the modelling of the tagger inputs for boosted $W$ bosons is studied in data using a $t\bar{t}$-enriched sample of events in 20.3 fb$^{-1}$ of data at $\sqrt{s}=8$ TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.
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2026 1verdicts
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Application of Deep Learning to Jet Charge Discrimination
Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.