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Optimization of reflectometry experiments using information theory

A framework based on Bayesian statistics and information theory is developed to optimize the design of surface-sensitive reflectometry experiments. The method applies to model-based reflectivity data analysis, uses simulated reflectivity data and is capable of optimizing experiments that probe a sam...

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Autores principales: Treece, Bradley W., Kienzle, Paul A., Hoogerheide, David P., Majkrzak, Charles F., Lösche, Mathias, Heinrich, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362612/
https://www.ncbi.nlm.nih.gov/pubmed/30800029
http://dx.doi.org/10.1107/S1600576718017016
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author Treece, Bradley W.
Kienzle, Paul A.
Hoogerheide, David P.
Majkrzak, Charles F.
Lösche, Mathias
Heinrich, Frank
author_facet Treece, Bradley W.
Kienzle, Paul A.
Hoogerheide, David P.
Majkrzak, Charles F.
Lösche, Mathias
Heinrich, Frank
author_sort Treece, Bradley W.
collection PubMed
description A framework based on Bayesian statistics and information theory is developed to optimize the design of surface-sensitive reflectometry experiments. The method applies to model-based reflectivity data analysis, uses simulated reflectivity data and is capable of optimizing experiments that probe a sample under more than one condition. After presentation of the underlying theory and its implementation, the framework is applied to exemplary test problems for which the information gain ΔH is determined. Reflectivity data are simulated for the current generation of neutron reflectometers at the NIST Center for Neutron Research. However, the simulation can be easily modified for X-ray or neutron instruments at any source. With application to structural biology in mind, this work explores the dependence of ΔH on the scattering length density of aqueous solutions in which the sample structure is bathed, on the counting time and on the maximum momentum transfer of the measurement. Finally, the impact of a buried magnetic reference layer on ΔH is investigated.
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spelling pubmed-63626122019-02-22 Optimization of reflectometry experiments using information theory Treece, Bradley W. Kienzle, Paul A. Hoogerheide, David P. Majkrzak, Charles F. Lösche, Mathias Heinrich, Frank J Appl Crystallogr Research Papers A framework based on Bayesian statistics and information theory is developed to optimize the design of surface-sensitive reflectometry experiments. The method applies to model-based reflectivity data analysis, uses simulated reflectivity data and is capable of optimizing experiments that probe a sample under more than one condition. After presentation of the underlying theory and its implementation, the framework is applied to exemplary test problems for which the information gain ΔH is determined. Reflectivity data are simulated for the current generation of neutron reflectometers at the NIST Center for Neutron Research. However, the simulation can be easily modified for X-ray or neutron instruments at any source. With application to structural biology in mind, this work explores the dependence of ΔH on the scattering length density of aqueous solutions in which the sample structure is bathed, on the counting time and on the maximum momentum transfer of the measurement. Finally, the impact of a buried magnetic reference layer on ΔH is investigated. International Union of Crystallography 2019-02-01 /pmc/articles/PMC6362612/ /pubmed/30800029 http://dx.doi.org/10.1107/S1600576718017016 Text en © Bradley W. Treece et al. 2019 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/
spellingShingle Research Papers
Treece, Bradley W.
Kienzle, Paul A.
Hoogerheide, David P.
Majkrzak, Charles F.
Lösche, Mathias
Heinrich, Frank
Optimization of reflectometry experiments using information theory
title Optimization of reflectometry experiments using information theory
title_full Optimization of reflectometry experiments using information theory
title_fullStr Optimization of reflectometry experiments using information theory
title_full_unstemmed Optimization of reflectometry experiments using information theory
title_short Optimization of reflectometry experiments using information theory
title_sort optimization of reflectometry experiments using information theory
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362612/
https://www.ncbi.nlm.nih.gov/pubmed/30800029
http://dx.doi.org/10.1107/S1600576718017016
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