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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Bayesian reconstruction of particle beam phase space

Bayesian reconstruction of particle beam phase space

Chapter:
(p.673) 33 Bayesian reconstruction of particle beam phase space
Source:
Bayesian Theory and Applications
Author(s):

C Nakhleh

D Higdon

C K Allen

R Ryne

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0033

This chapter discusses the application of Bayesian inversion methodology to reconstruct the initial phase space configuration of charged particle beams using a series of one-dimensional projection datasets (wirescans). It begins with a brief description of the proton beam produced by the Low Energy Demonstration Accelerator (LEDA) at the Los Alamos National Laboratory. It then describes the modelling approach, which uses process convolutions to represent the initial phase space configurations. This process convolution representation gives a parsimonious representation of the particle density as a function of spatial position and momentum, while ensuring positivity. Next, the chapter describes a Markov chain Monte Carlo (MCMC) scheme for producing posterior draws and a reconstruction for the initial phase space. It also outlines additional sensitivity studies to assess the impact of the prior smoothness on the resulting initial phase space reconstruction.

Keywords:   Bayesian inversion methodology, phase space configuration, particle beams, Low Energy Demonstration Accelerator, proton beams

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