Search for effective field theory parameters for H $\rightarrow$ ZZ* $\rightarrow$ 4 $\ell$ using Normalizing Flow models

The ATLAS and CMS collaborations announced the discovery of a new scalar particle of125 GeV mass in 2012 whose measured properties like production cross-sections, couplings with other Standard Model (SM) particles, charge-parity are consistent with thepredictions of SM within current measurement uncertainties. In the SM, interactions ofthe gauge bosons with Higgs boson lead to their masses via the mechanism of spontaneoussymmetry breaking. However, the SM does not explain the existence of dark matter anddominance of matter over anti-matter. Also, mass of the Higgs boson gets large corrections from quantum fluctuations in the SM theory. In absence of any direct evidence ofbeyond SM (BSM) physics, an alternative approach is to investigate BSM interactions ofHiggs boson modelled by an Effective Field Theory (EFT). An EFT incorporates higherdimensional interaction terms which could potentially modify the already observed SMinteractions. This thesis aims to explore machine learning techniques like normalizingflows based on a real-valued non-volume preserving approach to search for effects ofEFT interaction dubbed the cWW interaction on modifying the SM HZZ interactions infour lepton final state i.e. $H\to ZZ\ast \to \ell^{+}\ell^{-}\ell^{+}\ell^{-}$ where $\ell$ could be electron or muon. Theanalysis is implemented as a parameter estimation using likelihoods for which a completestatistical analysis is performed for the ML model.