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In this paper we study the gluino dijet mass edge measurement at the LHC in a realistic situation including both SUSY and combinatorical backgrounds together with effects of initial and final state radiation as well as a finite detector resolution. Three benchmark scenarios are examined in which the dominant SUSY production process and also the decay modes are different. Several new kinematical variables are proposed to minimize the impact of SUSY and combinatorial backgrounds in the measurement. By selecting events with a particular number of jets and leptons, we attempt to measure two distinct gluino dijet mass edges originating from wino $tilde g to jj tilde W$ and bino $tilde g to jj tilde B$ decay modes, separately. We determine the endpoints of distributions of proposed and existing variables and show that those two edges can be disentangled and measured within good accuracy, irrespective of the presence of ISR, FSR, and detector effects.
We consider a metapopulation version of the Schelling model of segregation over several complex networks and lattice. We show that the segregation process is topology independent and hence it is intrinsic to the individual tolerance. The role of the
We study the pattern of gluino cascade decays in a class of supersymmetric models where R-parity is spontaneously broken. The multi-lepton and same-sign dilepton rates in these models are compared with those of the Minimal Supersymmetric Standard Mod
We compute leading order quantum corrections to the Regge trajectory of a rotating string with massive endpoints using semiclassical methods. We expand the bosonic string action around a classical rotating solution to quadratic order in the fluctuati
We consider the application of endpoint techniques to the problem of mass determination for new particles produced at a hadron collider, where these particles decay to an invisible particle of unknown mass and one or more visible particles of known m
Extracting temporal relations (e.g., before, after, concurrent) among events is crucial to natural language understanding. Previous studies mainly rely on neural networks to learn effective features or manual-crafted linguistic features for temporal