Inference for a proton accelerator using convolution models

• 成果类型: Article
• 署名作者: Lee, Herbert K. H.; Sanso, Bruno; Zhou, Weining; Higdon, David M.
• 署名单位: University of California System; University of California Santa Cruz; Yahoo! Inc; United States Department of Energy (DOE); Los Alamos National Laboratory
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214507000000833
• 发表日期: 2008
• 页码: 604-613
• 关键词: field

摘要

Proton beams present difficulties in analysis because of the limited data that can be collected. The study of such beams must depend on complex computer simulators that incorporate detailed physical equations. The statistical problem of interest is to infer the initial state of the beam from the limited data collected as the beam passes through a series of focusing magnets. We are thus faced with a classic inverse problem where the computer simulator links the initial state to the observables. We propose a new model for the initial distribution that is derived from the discretized process convolution approach. This model provides a computationally tractable method for this highly challenging problem. Taking a Bayesian perspective allows better estimation of the uncertainty and propagation of this uncertainty.