Analysing Toponium at the LHC using Recursive Jigsaw Reconstruction

TL;DR

The paper tackles the challenge of identifying a toponium bound state near the $t\bar{t}$ threshold at the LHC by applying Recursive Jigsaw Reconstruction to the dileptonic $t\bar{t}$ channel, where two neutrinos create a combinatorial and kinematic puzzle. It constructs a decay-tree framework and four reconstruction strategies, leveraging rest-frame kinematics to derive discriminating observables and introducing new angular/frame variables $Nc_{hel}$ and $\Delta\Phi(t,\bar{t})$ to separate signal from SM background. Using NRQCD-based toponium samples generated with MadGraph5_aMC@NLO and backgrounds from $t\bar{t}$ at NLO, processed through Pythia8 and FastJet, the study demonstrates sizable gains in significance for Run 3, with optimal regions achieving up to $15.3\sigma$ before optimization and $14.1\sigma$ after optimization. The results indicate that Recursive Jigsaw Reconstruction provides robust, scale-sensitive observables that improve sensitivity to the quasi-bound toponium state and can inform threshold phenomenology and searches for additional near-threshold states.

Abstract

Recent results from the ATLAS and the CMS experiments at the Large Hadron Collider indicate the presence of a top-quark pair bound state near the threshold region. We present a way to reconstruct a toponium state at the $t\bar{t}$ threshold region formed at the Large Hadron Collider using the Recursive Jigsaw Reconstruction. We have considered the Non-Relativistic QCD based toponium model implemented in MadGraph5\_aMC@NLO. The final states considered consist of two b-jets, two oppositely charged leptons, and missing energy that arises from two neutrinos. The goal of the Recursive Jigsaw Reconstruction is to make use of rules that can help resolve combinatorics ambiguity in preparing the decay tree for a given physics event. Additionally, missing energy coming from two neutrinos needs to be resolved in order to reconstruct the event. We apply four different methods within the RestFrames package and compare the reconstruction results resulting from each of the methods. Due to this method, one can also access kinematic variables in the rest frames belonging to intermediate particle states, providing additional means to discriminate the SM $\ttbar$ background from the toponium signal. We propose using two angular variables to enhance sensitivity to the toponium signal. Our preliminary results indicate that the improvement in sensitivity can be as much as 16\% over the current strategy in the LHC's Run 3 configuration. This method may be useful for gaining additional insight into the physics phenomenology in the $\ttbar$ threshold region.

Figures (14)

• Figure 1: The $M_{t\bar{t}}$ mass at the truth level after applying pre-selection criteria.
• Figure 2: A decay tree diagram for the process $t\bar{t}\rightarrow bW(\ell\nu)bW(\ell\nu)$.
• Figure 3: Performance of the reconstruction methods in reconstructing $M_{t\bar{t}}$ (a) Method A (b) Method B (c) Method C and (d) Method D.
• Figure 4: Performance of the reconstruction methods in reconstructing $p_T$ of top quark (a) Method A (b) Method B (c) Method C and (d) Method D.
• Figure 5: Top quark mass as reconstructed by the four methods used in this analysis. Mass of top quark labelled as (Left) $ta$ and (Right) $tb$ in the RestFrames package after pre-selection criteria are applied.