Extracting a Toponium Signal at the LHC with Spin and Quantum Information Tools

Abstract

We investigate near-threshold top-antitop production at the LHC, focusing on the impact of toponium formation on spin correlations and quantum information properties of the final state. Considering the top-antitop system as a mixed two-qubit state, we reconstruct spin density matrices via quantum tomography and evaluate several observables including some inspired by quantum information. We then compare their sensitivity in discriminating toponium effects from top-antitop production without these effects. Our results demonstrate that combining these variables is expected to significantly enhance sensitivity to toponium effects, bringing new ways to explore these subtle features.

Figures (5)

• Figure 1: Distribution of the $t\bar{t}$ invariant mass for conventional top-antitop events without any toponium contributions (blue) and toponium-only events (red). Both distributions include the fiducial cuts discussed in Section \ref{['sec:sim']}, and in particular the selection $m_{t\bar{t}}<355$ GeV.
• Figure 2: Various distributions for conventional $t\bar{t}$ events without toponium effects (solid blue), toponium-only events (solid red) and $t\bar{t}$ events combined with the toponium contributions (dashed green). We present results for the $\Delta R$ (top left) and $\Delta\phi$ (top right) angular separation between the two final-state leptons, the $p^\star$ (middle left), $D^{(1)\mathrm{evt}}$ (middle right), $\cos \theta'$ (bottom left) and $\widetilde{M}^{\mathrm{evt}}_2$ (bottom right) observables. The results are normalised to $\mathcal{L}=140$ fb$^{-1}$ of LHC collisions at 13 TeV and include the selection cuts of Section \ref{['sec:sim']}.
• Figure 3: Distributions of the variables $b_1$, $b_2$, $b_3$ and $b_4$ for conventional $t\bar{t}$ events without toponium effects (solid blue), toponium-only events (solid red) and $t\bar{t}$ events combined with the toponium contributions (dashed green). The results are normalised to $\mathcal{L}=140$ fb$^{-1}$ of LHC collisions at 13 TeV and include the selection cuts of Section \ref{['sec:sim']}.
• Figure 4: Performance and correlations of the input variables used in our BDT analysis of the $t\bar{t}$ threshold region. Top left -- ROC curves for the individual observables, showing the $t\bar{t}$ background rejection $(1-\varepsilon_B)$ as a function of the toponium signal efficiency $(\varepsilon_S)$; the corresponding AUC values are given in the legend. Top right -- Correlation matrix of all BDT input variables after event selection. Bottom panels -- Feature importance obtained from the two BDT strategies. The bottom-left panel corresponds to events with $p^\star < 35$ GeV, where $p^\star$ is included among the inputs, while the bottom-right panel corresponds to events with $p^\star \geq 35$ GeV, where $p^\star$ is excluded.
• Figure 5: BDT performance for separating the toponium signal from the $t\bar{t}$ background, after the selection cuts of Section \ref{['sec:sim']}. Left panel -- BDT score distributions shown separately for the training and testing samples. Right panel -- ROC curves and AUC scores for different BDT input configurations including the full set of variables (solid blue), all variables with the exclusion of the quantum-information-inspired observables (dashed red), all variables with the exclusion of the $p^\star$ variable (dash-dotted green) and a configuration using only $D^{(1)\mathrm{evt}}$ and $\cos \theta'$ (dotted magenta). The kink in the two-variable configuration arises from the discrete structure of the tree-based classifier.