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Multimodal reconstruction of TbCo thin‐film structure with Bayesian analysis of polarized neutron reflectivity.

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Title: Multimodal reconstruction of TbCo thin‐film structure with Bayesian analysis of polarized neutron reflectivity.
Authors: Savchenkov, P. S.1,2 (AUTHOR) savch92@gmail.com, Nikolaev, K. V.2,3 (AUTHOR), Bodnarchuk, V. I.2,4,5 (AUTHOR), Pirogov, A. N.6 (AUTHOR), Belushkin, A. V.2,4,7 (AUTHOR), Yakunin, S. N.2 (AUTHOR)
Source: Journal of Applied Crystallography. Dec2024, Vol. 57 Issue 6, p1940-1950. 11p.
Subjects: Neutron reflectivity, Bayesian analysis, Magnetic structure, Thin films, Data analysis
Abstract: Bayesian analysis has been applied to polarized neutron reflectivity data. Reflectivity data from a magnetic TbCo thin‐film structure were studied using a combination of a Monte Carlo Markov‐chain algorithm, likelihood estimation and error modeling. By utilizing Bayesian analysis, it was possible to investigate the uniqueness of the solution beyond reconstructing the magnetic and structure parameters. The expedience of this approach has been demonstrated, as several probable reconstructions were found (the multimodality case) concerning the isotopic composition of the surface cover layer. Such multimodal reconstruction emphasizes the importance of rigorous data analysis instead of the direct data fitting approach, especially in the case of poor statistically conditioned data typical for neutron reflectivity experiments. This article presents details of the analysis and a discussion of multimodality. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Crystallography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 181259796
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  Data: Multimodal reconstruction of TbCo thin‐film structure with Bayesian analysis of polarized neutron reflectivity.
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  Data: <searchLink fieldCode="DE" term="%22Neutron+reflectivity%22">Neutron reflectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+structure%22">Magnetic structure</searchLink><br /><searchLink fieldCode="DE" term="%22Thin+films%22">Thin films</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Bayesian analysis has been applied to polarized neutron reflectivity data. Reflectivity data from a magnetic TbCo thin‐film structure were studied using a combination of a Monte Carlo Markov‐chain algorithm, likelihood estimation and error modeling. By utilizing Bayesian analysis, it was possible to investigate the uniqueness of the solution beyond reconstructing the magnetic and structure parameters. The expedience of this approach has been demonstrated, as several probable reconstructions were found (the multimodality case) concerning the isotopic composition of the surface cover layer. Such multimodal reconstruction emphasizes the importance of rigorous data analysis instead of the direct data fitting approach, especially in the case of poor statistically conditioned data typical for neutron reflectivity experiments. This article presents details of the analysis and a discussion of multimodality. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Applied Crystallography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1107/S1600576724010367
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 1940
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      – SubjectFull: Neutron reflectivity
        Type: general
      – SubjectFull: Bayesian analysis
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      – SubjectFull: Magnetic structure
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      – SubjectFull: Thin films
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      – SubjectFull: Data analysis
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              M: 12
              Text: Dec2024
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