Bayesian neural networks in data-intensive high energy physics applications |
Posted on:2015-09-23 | Degree:Ph.D | Type:Dissertation |
University:The Florida State University | Candidate:Perry, Michelle | Full Text:PDF |
GTID:1478390017497938 | Subject:Computer Science |
Abstract/Summary: | |
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. |
Keywords/Search Tags: | Bayesian neural networks, Processing unit, Minimal supersymmetric standard model |
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