Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry

Abstract Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of...

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Autores principales:	Hiroyuki Aoki, Yuwei Liu, Takashi Yamashita
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d168e5a353f1460c89ea1c0ecd10bf71
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id	oai:doaj.org-article:d168e5a353f1460c89ea1c0ecd10bf71
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spelling	oai:doaj.org-article:d168e5a353f1460c89ea1c0ecd10bf712021-11-28T12:17:38ZDeep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry10.1038/s41598-021-02085-62045-2322https://doaj.org/article/d168e5a353f1460c89ea1c0ecd10bf712021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02085-6https://doaj.org/toc/2045-2322Abstract Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of other sources such as synchrotron radiation; therefore, there has been a strong limitation in the time-resolved measurement and further advanced experiments such as surface imaging. This study aims at the development of a methodology to enable the structural analysis by the NR data with a large statistical error acquired in a short measurement time. The neural network-based method predicts the true NR profile from the data with a 20-fold lower signal compared to that obtained under the conventional measurement condition. This indicates that the acquisition time in the NR measurement can be reduced by more than one order of magnitude. The current method will help achieve remarkable improvement in temporally and spatially resolved NR methods to gain further insight into the surface and interfaces of materials.Hiroyuki AokiYuwei LiuTakashi YamashitaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hiroyuki Aoki Yuwei Liu Takashi Yamashita Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
description	Abstract Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of other sources such as synchrotron radiation; therefore, there has been a strong limitation in the time-resolved measurement and further advanced experiments such as surface imaging. This study aims at the development of a methodology to enable the structural analysis by the NR data with a large statistical error acquired in a short measurement time. The neural network-based method predicts the true NR profile from the data with a 20-fold lower signal compared to that obtained under the conventional measurement condition. This indicates that the acquisition time in the NR measurement can be reduced by more than one order of magnitude. The current method will help achieve remarkable improvement in temporally and spatially resolved NR methods to gain further insight into the surface and interfaces of materials.
format	article
author	Hiroyuki Aoki Yuwei Liu Takashi Yamashita
author_facet	Hiroyuki Aoki Yuwei Liu Takashi Yamashita
author_sort	Hiroyuki Aoki
title	Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
title_short	Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
title_full	Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
title_fullStr	Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
title_full_unstemmed	Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
title_sort	deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d168e5a353f1460c89ea1c0ecd10bf71
work_keys_str_mv	AT hiroyukiaoki deeplearningapproachforaninterfacestructureanalysiswithalargestatisticalnoiseinneutronreflectometry AT yuweiliu deeplearningapproachforaninterfacestructureanalysiswithalargestatisticalnoiseinneutronreflectometry AT takashiyamashita deeplearningapproachforaninterfacestructureanalysiswithalargestatisticalnoiseinneutronreflectometry
_version_	1718408103634927616

Deep learning approach for an interface structure analysis with a large statistical noise in neutron reflectometry

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