Studying Same-Sign Top Pair Production in Flavor Changing Scalar Models at the HL-LHC

TL;DR

This paper explores the potential of the HL-LHC to detect flavor-changing neutral current effects via same-sign top pair production, using machine learning to distinguish signals from background.

Contribution

It introduces a novel approach combining angular observables and machine learning to improve detection of top-Higgs FCNCs at the HL-LHC.

Findings

Potential to exclude FCNC branching ratios of order 10^{-4}

Machine learning enhances signal-background separation

Effective probing of top-Higgs FCNCs at HL-LHC

Abstract

We investigate the potential of the HL-LHC for discovering new physics effects via the same-sign top pair signatures. We focus on the semi leptonic (electron and muon) decay of the top quarks and study the reach for a simplified model approach where top quark flavor changing could occur through a neutral scalar exchange. A relatively smaller background contribution and clean signature are the advantages of the leptonic decay mode of the same-sign $W$ bosons in the same-sign production processes of top quark pairs. Assuming the FCNC between top quark, up type quark and scalar boson from the new physics interactions the branchings could be excluded of the order ${\cal O}(10^{-4})$. We use angular observables of the same-sign lepton pairs and the top quark kinematics in the process which provide the possibility of separation of new physics signal from the SM backgrounds using machine learning teqniques. We find that the same-sign top quark pair production is quite capable of testing the top-Higgs FCNCs at the HL-LHC.