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We clarify the relation between the variable MT2 and the method of kinematic constraints, both of which can be used for mass determination in events with two missing (dark matter) particles at hadron colliders. We identify a set of minimal kinematic constraints, including the mass shell conditions for the missing particles and their mother particles, as well as the constraint from the measured missing transverse momentum. We show that MT2 is the boundary of the mass region consistent with the minimal constraints. From this point of view, we also obtained a more efficient algorithm for calculating MT2. When more constraints are available in the events, we can develop more sophisticated mass determination methods starting from the MT2 constraint. In particular, we discuss cases when each decay chain contains two visible particles.
We re-examine the kinematic variable m_T2 and its relatives in the light of recent work by Cheng and Han. Their proof that m_T2 admits an equivalent, but implicit, definition as the `boundary of the region of parent and daughter masses that is kinema
Most SUSY searches at the LHC are optimised for the MSSM, where gauginos are Majorana particles. By introducing Dirac gauginos, we obtain an enriched phenomenology, from which considerable differences in the LHC signatures and limits are expected as
Decay channels with attractive branching ratios, or interesting physics, are recovered by substituting missing particles (nu, pi0, etc) with combined geometric and kinematic constraints. The Sliding Vertex method is shown in this part-I, for reconstructing strongly boosted B0s decays - at the LHC.
Reconstructed mass variables, such as $M_2$, $M_{2C}$, $M_T^star$, and $M_{T2}^W$, play an essential role in searches for new physics at hadron colliders. The calculation of these variables generally involves constrained minimization in a large param
We derive upper and lower bounds on the absorption of ultraintense laser light by solids as a function of fundamental laser and plasma parameters. These limits emerge naturally from constrained optimization techniques applied to a generalization of t