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Artificial intelligence (AI) and deep learning have made a full impact on society the last decades, including the realm of particle physics. This thesis explores whether a neural network, a deep learning program mimicking the biological brain, can be used to reject noise in real time at the Radio Neutrino Observatory in Greenland (RNO-G). RNO-G aims to detect radio waves in the ice cape of Greenland, induced by ultra high energy neutrinos ($>10^{18}$ eV). Due to the low flux of neutrinos at these energies, it is desired to increase the sensititivty of RNO-G by lowering the trigger threshold as much as possible. However, lowering the threshold is currently limited by unavoidable thermal noise fluctuations that would otherwise saturate the detector. Previous research has shown that a neural network could be used on a similar neutrino detector, ARIANNA, to reject thermal noise in real time, thus making it possible to lower the trigger threshold below the noise floor. This thesis aims to do the same for RNO-G.

The Standard Model of particle physics successfully describes present collider data. Nevertheless, theoretical and cosmological results call for its extension. A softly broken supersymmetric completion around the TeV scale solves several of the outstanding issues. Supersymmetry requires two Higgs doublets, leading to five physical Higgs states. These include a pair of charged Higgs bosons H±, which are a generic feature of theories with multiple Higgs doublets. Using results from high-energy colliders and flavour physics, constraints are derived on the charged Higgs boson mass and couplings; both for constrained scenarios in the minimal supersymmetric standard model (MSSM) with grand unification, and for general two-Higgs-doublet models. The MSSM results are compared to the projected reach for charged Higgs searches at the Large Hadron Collider (LHC). At the LHC, a light charged Higgs is accessible through top quark decay. Beyond a discovery, it is demonstrated how angular distributions sensitive to top quark spin correlations can be used to determine the structure of the H±tb coupling. The public code 2HDMC, which performs calculations in a general, CP-conserving, two-Higgs-doublet model, is introduced. In parallel to the developments at colliders, the most energetic particles ever recorded are the ultra-high-energy (UHE) cosmic rays. To gain more insight into their origin, new experiments are searching for UHE neutrinos. These searches require detectors of vast volume, which can be achieved by searching for coherent radio pulses arising from the Askaryan effect. The prospects of using a satellite orbiting the Moon to search for neutrino interactions are investigated, and a similar study for an Earth-based radio telescope is presented. In both cases, the method is found competitive for detection of the very highest energy neutrinos considered.