Speaker
Description
Magnetic monopoles are beyond standard model particles, predicted by Grand Unified Theories (GUTs) to be created during the early universe. At typical masses of the GUT-scale - above $10^{14}$ GeV - these particles would move at sub-relativistic speeds. The Rubakov-Callan effect predicts that magnetic monopoles can catalyze nucleon decays, in particular the decay of protons. This results in a unique signature of small particle cascades along the trajectory of the slow moving magnetic monopole. Since 2012, a dedicated Slow-Particle Filter has been implemented in the IceCube Neutrino Observatory for the detection of magnetic monopoles. Current limits set an upper bound for the monopole flux at $\Phi_{\mathrm{90}}\leq 10^{-17}$ to $10^{-18} \mathrm{cm}^{-2}\mathrm{s}^{-1}\mathrm{sr}^{-1}$ depending on the catalysis cross section for the proton decay. A detection of the monopole flux thus requires exceptional background rejection and signal efficiency. This is accomplished using machine learning methods. In this analysis, we use a multi-level boosted decision tree classifier. We present the strategy behind the background and signal simulation, the classification efficiency, and IceCube’s projected sensitivity for the detection of sub-relativistic magnetic monopoles.
| Collaboration(s) | IceCube |
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