Hauptseite > Publikationsdatenbank > Deep learning for X-ray or neutron scattering under grazing-incidence: extraction of distributions > print

001     889108
005     20220930130302.0
024 7 _ |a 10.1088/2053-1591/abd590
|2 doi
024 7 _ |a 2128/27685
|2 Handle
024 7 _ |a WOS:000642500400001
|2 WOS
037 _ _ |a FZJ-2021-00038
082 _ _ |a 620
100 1 _ |a Van Herck, Walter
|0 P:(DE-Juel1)145760
|b 0
245 _ _ |a Deep learning for X-ray or neutron scattering under grazing-incidence: extraction of distributions
260 _ _ |a Bristol
|c 2021
|b IOP Publ.
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1648131933_25708
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Grazing-incidence small-angle scattering (GISAS) is a technique of significant importance for the investigation of thin multilayered films containing nano-sized objects. It provides morphology information averaged over the sample area. However, this averaging together with multiple reflections and the well-known phase problem make the data analysis challenging and time consuming. In the present paper we show that densely connected neural networks (DenseNets) can be applied for GISAS data analysis and deliver fast and plausible results. The extraction of the rotational distributions of hexagonal nanoparticle arrangements is taken as a case study.
536 _ _ |a 6G4 - Jülich Centre for Neutron Research (JCNS) (FZJ) (POF4-6G4)
|0 G:(DE-HGF)POF4-6G4
|c POF4-6G4
|f POF IV
|x 0
536 _ _ |a 623 - Data Management and Analysis (POF4-623)
|0 G:(DE-HGF)POF4-623
|c POF4-623
|f POF IV
|x 1
588 _ _ |a Dataset connected to CrossRef
650 2 7 |a Instrument and Method Development
|0 V:(DE-MLZ)SciArea-220
|2 V:(DE-HGF)
|x 0
650 1 7 |a Instrument and Method Development
|0 V:(DE-MLZ)GC-2002-2016
|2 V:(DE-HGF)
|x 0
693 _ _ |0 EXP:(DE-MLZ)SCG-20150203
|5 EXP:(DE-MLZ)SCG-20150203
|e SCG: Scientific Computing Group
|x 0
700 1 _ |a Fisher, Jonathan
|0 P:(DE-Juel1)169150
|b 1
700 1 _ |a Ganeva, Marina
|0 P:(DE-Juel1)161101
|b 2
|e Corresponding author
773 _ _ |a 10.1088/2053-1591/abd590
|0 PERI:(DE-600)2760382-9
|p 04515
|t Materials Research Express
|v 8
|y 2021
|x 2053-1591
856 4 _ |u https://juser.fz-juelich.de/record/889108/files/8166209_0.pdf
856 4 _ |u https://juser.fz-juelich.de/record/889108/files/Van_Herck_2021_Mater._Res._Express_8_045015.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:889108
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB:MLZ
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)161101
913 1 _ |a DE-HGF
|b Forschungsbereich Materie
|l Großgeräte: Materie
|1 G:(DE-HGF)POF4-6G0
|0 G:(DE-HGF)POF4-6G4
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-600
|4 G:(DE-HGF)POF
|v Jülich Centre for Neutron Research (JCNS) (FZJ)
|x 0
913 1 _ |a DE-HGF
|b Forschungsbereich Materie
|l Matter and Technologies
|1 G:(DE-HGF)POF4-620
|0 G:(DE-HGF)POF4-623
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-600
|4 G:(DE-HGF)POF
|v Data Management and Analysis
|x 1
913 0 _ |a DE-HGF
|b Key Technologies
|l Future Information Technology - Fundamentals, Novel Concepts and Energy Efficiency (FIT)
|1 G:(DE-HGF)POF3-520
|0 G:(DE-HGF)POF3-524
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Controlling Collective States
|x 0
913 0 _ |a DE-HGF
|b Forschungsbereich Materie
|l Von Materie zu Materialien und Leben
|1 G:(DE-HGF)POF3-620
|0 G:(DE-HGF)POF3-621
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-600
|4 G:(DE-HGF)POF
|v In-house research on the structure, dynamics and function of matter
|9 G:(DE-HGF)POF3-6212
|x 1
913 0 _ |a DE-HGF
|b Forschungsbereich Materie
|l Großgeräte: Materie
|1 G:(DE-HGF)POF3-6G0
|0 G:(DE-HGF)POF3-6G15
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-600
|4 G:(DE-HGF)POF
|v FRM II / MLZ
|x 2
913 0 _ |a DE-HGF
|b Forschungsbereich Materie
|l Von Materie zu Materialien und Leben
|1 G:(DE-HGF)POF3-620
|0 G:(DE-HGF)POF3-623
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-600
|4 G:(DE-HGF)POF
|v Facility topic: Neutrons for Research on Condensed Matter
|9 G:(DE-HGF)POF3-6G4
|x 3
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-08-22
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MATER RES EXPRESS : 2018
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2020-08-22
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2020-08-22
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-08-22
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2020-08-22
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2020-08-22
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-08-22
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-08-22
920 1 _ |0 I:(DE-Juel1)JCNS-FRM-II-20110218
|k JCNS-FRM-II
|l JCNS-FRM-II
|x 0
920 1 _ |0 I:(DE-588b)4597118-3
|k MLZ
|l Heinz Maier-Leibnitz Zentrum
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JCNS-FRM-II-20110218
980 _ _ |a I:(DE-588b)4597118-3
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21