ﻻ يوجد ملخص باللغة العربية
In this paper we consider an $S^{1}/mathbb{Z}_2$ compactified flat extra dimensional scenario where all the standard model states can access the bulk and have generalised brane localised kinetic terms. The flavour structure of brane kinetic terms for the standard model fermions are dictated by stringent flavour bounds on the first two generations implying an $U(2)_{Q_L} otimes U(2)_{u_R} otimes U(2)_{d_R}$ flavour symmetry. We consider the constraints on such a scenario arising from dark matter relic density and direct detection measurements, precision electroweak data, Higgs physics and LHC dilepton searches. We discuss the possibility of such a scenario providing an explanation of the recently measured anomaly in $R_{K^{(ast)}}$ within the allowed region of the parameter space.
We estimate contributions from Kaluza-Klein excitations of third generation quarks and gauge bosons to the branching ratio of $Brightarrow X_sgamma$ decay process in 5-Dimensional Universal Extra Dimensional scenario with non-vanishing boundary local
We estimate contributions from Kaluza-Klein excitations of gauge bosons and physical charge scalar for the explanation of the lepton flavor universality violating excess in the ratios $mathcal{R}(D)$ and $mathcal{R}(D^*)$ in 5 dimensional Universal E
Universal Extra Dimension (UED) is a well-motivated and well-studied scenario. One of the main motivations is the presence of a dark matter (DM) candidate namely, the lightest level-1 Kaluza-Klein (KK) particle (LKP), in the particle spectrum of UED.
We discuss prospects of the $Z$ search at the LHC in non-minimal Universal Extra Dimensions with tree-level brane-local terms in five dimensions. In this scenario, we find two major differences from the usual $Z$ physics: (i) two $Z$ candidates close
In theories with Universal Extra-Dimensions (UED), the gamma_1 particle, first excited state of the hypercharge gauge boson, provides an excellent Dark Matter (DM) candidate. Here we use a modified version of the SuperBayeS code to perform a Bayesian