SAGE, a dual-branch GNN trained under nuisance fluctuations, estimates the Higgs signal strength with near-nominal coverage (0.662-0.683) but wider intervals than the top FAIR-HUC leaderboard methods.
Differential cross-section measurements of Higgs boson production in the $H\to\tau^+\tau^-$ decay channel in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
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abstract
Differential measurements of Higgs boson production in the $\tau$-lepton-pair decay channel are presented in the gluon fusion, vector-boson fusion (VBF), $VH$ and $t\bar{t}H$ associated production modes, with particular focus on the VBF production mode. The data used to perform the measurements correspond to 140 fb$^{-1}$ of proton-proton collisions collected by the ATLAS experiment at the LHC. Two methods are used to perform the measurements: the Simplified Template Cross-Section (STXS) approach and an Unfolded Fiducial Differential measurement considering only the VBF phase space. For the STXS measurement, events are categorized by their production mode and kinematic properties such as the Higgs boson's transverse momentum ($p^{\text{H}}_\text{T}$), the number of jets produced in association with the Higgs boson, or the invariant mass of the two leading jets ($m_{jj}$). For the VBF production mode, the ratio of the measured cross-section to the Standard Model prediction for $m_{jj}>1.5$ TeV and $p^{\text{H}}_\text{T}>200$ GeV ($p^{\text{H}}_\text{T}<200$ GeV) is ${1.29}^{+0.39}_{-0.34}$ (${0.12}^{+0.34}_{-0.33}$). This is the first VBF measurement for the higher-$p^{\text{H}}_\text{T}$ criteria, and the most precise for the lower-$p^{\text{H}}_\text{T}$ criteria. The fiducial cross-section measurements, which only consider the kinematic properties of the event, are performed as functions of variables characterizing the VBF topology, such as the signed $\Delta\phi_{jj}$ between the two leading jets. The measurements have a precision of 30%-50% and agree well with the Standard Model predictions. These results are interpreted in the SMEFT framework, and place the strongest constraints to date on the CP-odd Wilson coefficient $c_{H\tilde{W}}$.
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Higgs Signal Strength Estimation with Machine Learning under Systematic Uncertainties
SAGE, a dual-branch GNN trained under nuisance fluctuations, estimates the Higgs signal strength with near-nominal coverage (0.662-0.683) but wider intervals than the top FAIR-HUC leaderboard methods.