Speaker
Description
The search for weakly interacting particles is one of the main objectives of the high luminosity LHC. In the Minimal Supersymmetric Extension of the Standard Model (MSSM), these particles include the lightest neutralino, which is a good Dark Matter candidate and whose relic density may be fixed to realistic values through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, its direct Dark Matter detection rate is suppressed for the same region of parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This motivates the search for radiatively decaying neutralinos, which, however, suffers from strong backgrounds. In this work we provide an analysis of the reach of the LHC in the search for these radiatively decaying particles by means of cut-based and machine learning methods, defining the LHC discovery potential in this well motivated region of parameters in the high luminosity era.
