TITLE:
Neural Networks Search for Charged Higgs Boson in Two Doublet Higgs Model at the Hadrons Colliders
AUTHORS:
Nady Bakhet, Maxim Yu Khlopov, Tarek Hussein
KEYWORDS:
2HDM, Neural Networks, Pythia8, MadGraph5, CalcHep, ROOT
JOURNAL NAME:
Open Journal of Microphysics,
Vol.6 No.1,
December
30,
2015
ABSTRACT: In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by existence scalar sector with new five Higgs bosons; two of them are electrically charged and the other three Higgs bosons are neutral charged. Our analysis based on the Monte Carlo data produced from the simulation of 2HDM with proton antiproton collisions at the Tevatron = 1.96 TeV (Fermi Lab) and proton proton collisions at the LHC = 14 TeV (CERN) with final state includes electron, muon, multiple jets and missing transverse energy via the production and decay of the new Higgs in the hard process where the dominant background (electrons and muons) for this process comes from the Standard Model processes via the production and decay of top quark pair. We assumed that the branching ratio of charged Higgs boson to tau lepton and neutrino is 100%. We used the Artificial Neural Networks (ANNs) which are an efficient technique to discriminate the signal of charged Higgs boson from the SM background for charged Higgs boson masses between 80 GeV and 160 GeV. Also we calculated the production cross section at different energies, decay width, branching ration and different kinematics distribution for charged Higgs boson and for the final state particles.