28–30 May 2024
Universidad Complutense de Madrid (UCM)
Europe/Madrid timezone

Machine-Learning Collider Analysis of Radiative Neutralino Decays at the LHC

Not scheduled
20m
Universidad Complutense de Madrid (UCM)

Universidad Complutense de Madrid (UCM)

Rooms M1 and M2

Speaker

Dr Rosa María Sandá Seoane (Instituto de Física Teórica UAM-CSIC)

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 (DM) 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 DM 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, for a center-of-mass energy of 14 TeV and a luminositiy of 100 fb$^{-1}$, 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.

Authors

Dr Andres Daniel Perez (Instituto de Física Teórica UAM-CSIC) Carlos Wagner (The University of Chicago) Duncan Rocha Dr Ernesto Arganda (Instituto de Física Teórica UAM-CSIC) Marcela Carena Dr Martín de los Rios (IFT/UAM) Dr Rosa María Sandá Seoane (Instituto de Física Teórica UAM-CSIC)

Presentation materials