A normalizing-flow-based Bayesian unfolding method (NPU) plus a modern Python implementation of Fully Bayesian Unfolding (FBU) are introduced and validated on Gaussian and simulated LHC jet data.
Measurements of top quark spin observables in $t\bar{t}$ events using dilepton final states in $\sqrt{s} = 8$ TeV $pp$ collisions with the ATLAS detector
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Measurements of top quark spin observables in $t\bar{t}$ events are presented based on 20.2 fb$^{-1}$ of $\sqrt{s} = 8$ TeV proton-proton collisions recorded with the ATLAS detector at the LHC. The analysis is performed in the dilepton final state, characterised by the presence of two isolated leptons (electrons or muons). There are 15 observables, each sensitive to a different coefficient of the spin density matrix of $t\bar{t}$ production, which are measured independently. Ten of these observables are measured for the first time. All of them are corrected for detector resolution and acceptance effects back to the parton and stable-particle levels. The measured values of the observables at parton level are compared to Standard Model predictions at next-to-leading order in QCD. The corrected distributions at stable-particle level are presented and the means of the distributions are compared to Monte Carlo predictions. No significant deviation from the Standard Model is observed for any observable.
citation-role summary
citation-polarity summary
fields
hep-ph 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Neural Posterior Unfolding
A normalizing-flow-based Bayesian unfolding method (NPU) plus a modern Python implementation of Fully Bayesian Unfolding (FBU) are introduced and validated on Gaussian and simulated LHC jet data.