UC Irvine

Several theoretical parameter spaces are analysed using techniques from machine learning. First, machine learning is used to leverage high dimensional detector information to constrain the mass and coupling of a simplified model which extends the standard model with a heavy Z' boson. The high dimensional information is seen to improve the exclusion contours of the analysis relative to a low dimensional analysis that instead uses summary statistics. Next, generative models are used to sample a high dimensional parameter space of a supersymmetric model subject to a constraint. Generative models are seen to provide an increase in efficiency of over an order of magnitude in a search for models that satisfy the constraint when compared to random sampling. Finally, we analyse a dataset of supersymmetric theories to understand how they may best discovered in experiment. We propose a set of experiments to be performed at the Large Hadron Collider that are most sensitive to these models.