Constraining some SMEFT operator using LHC data

Abstract

The discovery of the Higgs boson at the Large Hadron Collider (LHC) is the crowning achievement of the Standard Model (SM) of particle physics. The SM is now proven to be a consistent field theory describing a vast range of phenomena at the electroweak scale and below.

In this success of theoretical and experimental efforts, however, lies a pressing question: what is the underlying physics at length scales shorter than the electroweak (EW) scale ~100 GeV^-1? Or turning it around, till what length scale are the rules of SM valid? One way to study this question in a relatively model independent approach is to look for non-zero coefficients of local higher dimensional operators of mass dimension greater than four. These coefficients, the so-called Wilson Coefficients (WCs), can be directly constrained from experimental data collected at the LHC. Another approach to put bounds on these WCs is developed using Machine Learning.

In the master's thesis, my aim is to put bounds on some WC of the Standard Model Effective Field Theory (SMEFT), using processes involving the top quark. This thesis sheds light on some preliminary theory, followed by Cut and Count analysis to obtain bounds on the operator, and then methods in Machine Learning to improve the bound. For Λ=1 TeV, the strongest bound at ℒ=35.8 fb^-1 obtained using Cut and Count analysis with invariant mass-top transverse momentum distribution is C_G∈[-0.259,0.225].