Optimization of reflectometry experiments using information theory.
experimental optimization
information content
neutron reflectometry
Journal
Journal of applied crystallography
ISSN: 0021-8898
Titre abrégé: J Appl Crystallogr
Pays: United States
ID NLM: 9876190
Informations de publication
Date de publication:
01 Feb 2019
01 Feb 2019
Historique:
received:
02
08
2018
accepted:
30
11
2018
entrez:
26
2
2019
pubmed:
26
2
2019
medline:
26
2
2019
Statut:
epublish
Résumé
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 Δ
Identifiants
pubmed: 30800029
doi: 10.1107/S1600576718017016
pii: ge5055
pmc: PMC6362612
doi:
Types de publication
Journal Article
Langues
eng
Pagination
47-59Références
Langmuir. 2009 Apr 7;25(7):4132-44
pubmed: 19714896
IUCrJ. 2015 Apr 21;2(Pt 3):352-60
pubmed: 25995844
Nat Protoc. 2014 Feb;9(2):439-56
pubmed: 24457334
Soft Matter. 2009;5(13):2576-2586
pubmed: 21311730
Adv Colloid Interface Sci. 2000 Dec 11;88(1-2):243-74
pubmed: 11185700
Langmuir. 2009 Apr 7;25(7):4154-61
pubmed: 19714897
J Appl Crystallogr. 2016 Jun 09;49(Pt 4):1121-1129
pubmed: 27504074
Rev Sci Instrum. 2006 Jul;77(7):74301-7430111
pubmed: 21892232
PLoS Comput Biol. 2013;9(1):e1002888
pubmed: 23382663
Biochim Biophys Acta. 2014 Sep;1838(9):2341-9
pubmed: 24674984