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\sect{Introduction}
With the discovery of the Higgs, the Standard Model seems to offer a complete tally of the fundamental particles with which all of the matter around us is made [cite higgs discovery].
However, with this answer comes more questions as phenomena such as the origin of dark matter remain unanswered, leading theorist to look for physics “beyond the Standard Model”.
Supersymmetry is such a theory that proposes an extension to spacetime which allows for a symmetry relating the two groups of fundamental particles: fermions and bosons. Associated with this extension is a quantum number R_p defined as
R_p = (-1)^(3B + L + 2S)
where S, B, and L, are the spin, baryon, and lepton quantum numbers of the particle [LEP].
In order for R-parity to be conserved, supersymmetric particles must decay through a series of intermediate processes and have an eventual final state of lighter Standard particles and some amount of the lightest supersymmetric particle (LSP) [LEP]. However, if R-parity is not conserved, a supersymmetric particle can have a final state which consists only of particles found in the Standard Model.
While there have been a few searches for R-parity violating decays in the past, R-parity conserving theories are more widely researched as they provide an explanation for the massive prevalence of seemingly weakly interacting dark matter. [LEP][Others][LSP as DM cand] However, there is little reason to believe a-priori that spacetime would behave in such a way as to conserve R_p. Coupling this with the increased capacity for data collection available to the CMS at LHC at CERN, it is necessary to perform searches for these decays using new techniques which might uncover possibly interesting results.
This paper describes a search for such a decay of the form:
\widetilde{t}\overline{\widetilde{t}} \rightarrow \mu^+ \mu ^- b \overline{b}
Since the signature of this decay resembles that of many common Standard Model vertices, various data-based background estimation techniques were implemented in conjunction with Monte Carlo simulations to obtain a number of expected events. For a detailed description of these techniques see section \ref{sect:methods}.
