Bayesian Neural Networks in Data-Intensive High Energy Physics Applications

• Other Authors: Perry, Michelle (authoraut)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Numerical analysis
• Online Access: http://purl.flvc.org/fsu/fd/FSU_migr_etd-8867

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. === A Dissertation submitted to the Department of Scientific Computing in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Spring Semester, 2014. === April 1, 2014. === Bayesian Neural Networks, GPU, pMSSM, Scientific Computing === Includes bibliographical references. === Anke Meyer-Baese, Professor Directing Dissertation; Harrison Prosper, Professor Directing Dissertation; Jorge Piekarewicz, University Representative; Sachin Shanbhag, Committee Member; Peter Beerli, Committee Member.