Graduate Program Events |
Several new physics models predict particles that are expected to leave signatures of missing transverse momentum in collider experiments. One of the primary motivations for such searches is the astrophysical evidence for dark matter, including galactic rotation curves, gravitational lensing, and observations of the cosmic microwave background. Weakly Interacting Massive Particles (WIMPs) are a leading candidate for dark matter, and this thesis explores the parameter space of two WIMP-inspired models, setting stringent limits on their viability. In addition, searches for extra spacetime dimensions, leptoquarks, and quantum blackholes are also performed. Machine learning techniques are used for these searches.
This thesis also presents an algorithm to mitigate beam-induced background in a future muon collider using fast machine learning