Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

Michelle Perry
Florida State University, College of Arts and Sciences
Florida State University, 2014


   title={Bayesian Neural Networks in Data-Intensive High Energy Physics Applications},

   author={Perry, Michelle},




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This dissertation studies a graphical processing unit (GPU) construction of Bayesian neural networks (BNNs) using large training data sets. The goal is to create a program for the mapping of phenomenological Minimal Supersymmetric Standard Model (pMSSM) parameters to their predictions. This would allow for a more robust method of studying the Minimal Supersymmetric Standard Model, which is of much interest at the Large Hadron Collider (LHC) experiment CERN. A systematic study of the speedup achieved in the GPU application compared to a Central Processing Unit (CPU) implementation are presented.
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