On the BSM reach of four top production at the LHC

TL;DR

This paper explores the potential of four top quark final states at the LHC for probing BSM physics, highlighting the advantages of advanced analysis techniques and interference effects in enhancing discovery sensitivity.

Contribution

It demonstrates the use of Graph Neural Networks for improved SM potential and shows that four top final states mitigate interference issues, increasing BSM discovery prospects.

Findings

Graph Neural Networks improve signal detection.

Four top states reduce destructive interference effects.

Significant exclusion potential for two Higgs doublet models.

Abstract

Many scenarios of beyond the Standard Model (BSM) physics give rise to new top-philic interactions that can be probed at proton machines such as the Large Hadron Collider through a variety of production and decay modes. On the one hand, this will enable a detailed determination of the BSM model's parameters when a discovery is made and additional sensitivity in non-dominant production modes can be achieved. On the other hand, the naive narrow width approximation in dominant production modes such as gluon fusion might be inadequate for some BSM parameter regions due to interference effects, effectively making less dominant production modes more relevant in such instances. In this work, we consider both these questions in the context of four top quark final states at the LHC. Firstly, we show that the SM potential can be enhanced through the application of targeted Graph Neural Network techniques that exploit data correlations beyond cut-and-count approaches. Secondly, we show that destructive interference effects that can degrade BSM sensitivity of top-philic states from gluon fusion are largely avoided by turning to four top final states. This achieves considerable exclusion potential for, e.g., the two Higgs doublet model. This further motivates four top final states as sensitive tools for BSM discovery in the near future of the LHC.