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Blokland, W.

Paper Title Page
WGF13 Extracting Information Content within Noisy, Sampled Profile Data from Charged Particle Beams 479
 
  • C.K. Allen, W. Blokland, S.M. Cousineau, J. Galambos
    ORNL, Oak Ridge, Tennessee
 
 

Charged-particle beam diagnostic devices such as wire scanners and wire harps provide data sets describing the one-dimensional density distributions at a particular location; these data are commonly called profile data. We use these data for further computations, usually beam properties such as position and size. Typically these data require subjective, human, processing to extract meaningful results; this is inefficient and labor intensive. Our ultimate goal is to automate these computations, at least streamline the process. If we hope to implement any type of automation we must make real world considerations. Specifically, we consider information content, noise in the data, and sampling theory. Within this framework we create a general model for the data sets. Using signal processing techniques we identify the minimal sampling requirements for maintaining information content. Using Bayesian analysis we identify the most probable Gaussian signal within the data. We present the major obstacles currently faced concerning robust automation techniques.

 

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