Kavli Affiliate: Marcela Carena
| First 5 Authors: Ernesto Arganda, Marcela Carena, MartÃn de los Rios, Andres D. Perez, Duncan Rocha
| Summary:
The search for weakly interacting matter particles (WIMPs) is one of the main
objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work
we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a
Dark Matter (DM) candidate in a supersymmetric framework. The minimal
supersymmetric WIMP sector includes the lightest neutralino that can provide
the observed DM relic density through its co-annihilation with the second
lightest neutralino and lightest chargino. Moreover, the direct DM detection
cross section rates fulfill current experimental bounds and provide discovery
targets for the same region of model parameters in which the radiative decay of
the second lightest neutralino into a photon and the lightest neutralino is
enhanced. This strongly motivates the search for radiatively decaying
neutralinos which, however, suffers from strong backgrounds. We investigate the
LHC reach in the search for these radiatively decaying particles by means of
cut-based and ML methods and estimate its discovery potential in this
well-motivated, new physics scenario.
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