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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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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 22 April 2021

Bayesian reconstruction of particle beam phase space

Bayesian reconstruction of particle beam phase space

(p.673) 33 Bayesian reconstruction of particle beam phase space
Bayesian Theory and Applications

C Nakhleh

D Higdon

C K Allen

R Ryne

Oxford University Press

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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