A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data
Energy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected x-ray must be leveraged to obtain the maxi...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/add8e1 |
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| author | Pau Torruella Abderrahim Halimi Ludovica Tovaglieri Céline Lichtensteiger Duncan T L Alexander Cécile Hébert |
| author_facet | Pau Torruella Abderrahim Halimi Ludovica Tovaglieri Céline Lichtensteiger Duncan T L Alexander Cécile Hébert |
| author_sort | Pau Torruella |
| collection | DOAJ |
| description | Energy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected x-ray must be leveraged to obtain the maximum possible amount of information about the sample. To this end, we introduce a robust multiscale Bayesian approach that accounts for the Poisson statistics in the EDX data and leverages their underlying spatial correlations. This is combined with EDX spectral simulation (elemental contributions and Bremsstrahlung background) into a Bayesian estimation strategy. When tested using simulated datasets, the chemical maps obtained with this approach are more accurate and preserve a higher spatial resolution than those obtained by standard methods. These properties translate to experimental datasets, where the method enhances the atomic resolution chemical maps of a canonical tetragonal ferroelectric PbTiO _3 sample, such that ferroelectric domains are mapped with unit-cell resolution. |
| format | Article |
| id | doaj-art-a571f99ba93a45c381d33fee3f41d699 |
| institution | Kabale University |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-a571f99ba93a45c381d33fee3f41d6992025-08-20T03:47:58ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016202504310.1088/2632-2153/add8e1A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray dataPau Torruella0https://orcid.org/0000-0002-6864-4000Abderrahim Halimi1https://orcid.org/0000-0002-8112-5352Ludovica Tovaglieri2https://orcid.org/0009-0009-3226-3888Céline Lichtensteiger3https://orcid.org/0000-0002-9796-8071Duncan T L Alexander4https://orcid.org/0000-0003-4350-8587Cécile Hébert5https://orcid.org/0000-0002-7086-1901Electron Spectrometry and Microscopy Laboratory (LSME), Institute of Physics (IPHYS), Ecole Polytechnique Fédérale de Lausanne (EPFL) , Lausanne, SwitzerlandSchool of Engineering and Physical Sciences, Heriot-Watt University , Edinburgh, United KingdomDepartment of Quantum Matter Physics, University of Geneva , Geneva, SwitzerlandDepartment of Quantum Matter Physics, University of Geneva , Geneva, SwitzerlandElectron Spectrometry and Microscopy Laboratory (LSME), Institute of Physics (IPHYS), Ecole Polytechnique Fédérale de Lausanne (EPFL) , Lausanne, SwitzerlandElectron Spectrometry and Microscopy Laboratory (LSME), Institute of Physics (IPHYS), Ecole Polytechnique Fédérale de Lausanne (EPFL) , Lausanne, SwitzerlandEnergy dispersive x-ray (EDX) spectrum imaging yields compositional information with a spatial resolution down to the atomic level. However, experimental limitations often produce extremely sparse and noisy EDX spectra. Under such conditions, every detected x-ray must be leveraged to obtain the maximum possible amount of information about the sample. To this end, we introduce a robust multiscale Bayesian approach that accounts for the Poisson statistics in the EDX data and leverages their underlying spatial correlations. This is combined with EDX spectral simulation (elemental contributions and Bremsstrahlung background) into a Bayesian estimation strategy. When tested using simulated datasets, the chemical maps obtained with this approach are more accurate and preserve a higher spatial resolution than those obtained by standard methods. These properties translate to experimental datasets, where the method enhances the atomic resolution chemical maps of a canonical tetragonal ferroelectric PbTiO _3 sample, such that ferroelectric domains are mapped with unit-cell resolution.https://doi.org/10.1088/2632-2153/add8e1multiscaleBayesiandenoisingenergy dispersive x-ray spectroscopychemical quantificationelectron microscopy |
| spellingShingle | Pau Torruella Abderrahim Halimi Ludovica Tovaglieri Céline Lichtensteiger Duncan T L Alexander Cécile Hébert A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data Machine Learning: Science and Technology multiscale Bayesian denoising energy dispersive x-ray spectroscopy chemical quantification electron microscopy |
| title | A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data |
| title_full | A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data |
| title_fullStr | A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data |
| title_full_unstemmed | A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data |
| title_short | A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data |
| title_sort | multiscale bayesian approach to quantification and denoising of energy dispersive x ray data |
| topic | multiscale Bayesian denoising energy dispersive x-ray spectroscopy chemical quantification electron microscopy |
| url | https://doi.org/10.1088/2632-2153/add8e1 |
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