UC Santa Cruz

Four different Binary Classification Neural Networks are used to assess the sensitivity of experiments at the CERN Large Hadron Collider related to Supersymmetry. Several methods are used to study the effectiveness of each neural network's ability to separate signal from background by evaluating their performance during and after the training phase. Sensitivities over the slepton's parameter space are graphed using each of the four neural networks. The four neural networks are then evaluated individually and comparatively in order to analyze each of the neural network's respective performance and general trends in sensitivity to the slepton's parameter space.