Neural-network–based algorithm for the inverse problem of measuring K-shell ionization cross-sections of Si induced by 3–25 keV electrons and 4.5–9 keV positrons using the thick-target method
In this study, a neural network method is proposed for solving the inverse problem in the measurement of inner-shell ionization cross-sections using the thick-target method. It was applied to calculate the K -shell ionization cross-section of silicon (Si) from positron impacts in the energy range fr...
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| Published in | Europhysics letters Vol. 143; no. 6; pp. 65003 - 65009 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Les Ulis
EDP Sciences, IOP Publishing and Società Italiana di Fisica
01.09.2023
IOP Publishing |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0295-5075 1286-4854 1286-4854 |
| DOI | 10.1209/0295-5075/acf60b |
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| Abstract | In this study, a neural network method is proposed for solving the inverse problem in the measurement of inner-shell ionization cross-sections using the thick-target method. It was applied to calculate the
K
-shell ionization cross-section of silicon (Si) from positron impacts in the energy range from 4.5 to 9 keV, using a Monte Carlo simulation program called PENELOPE to construct a comprehensive characteristic X-ray yield and cross-section database, serving as a foundation for training the neural network. The experimental values are compared with those obtained using regularization, yield differential, and distorted-wave Born approximation (DWBA) theoretical models. Our findings reveal that the cross-section results obtained from all three algorithms are in good agreement with the theoretical DWBA values within the error range. Moreover, our study highlights the superiority of the neural network algorithm in solving ill-posed problems, compared with traditional regularization algorithms and the yield differential method. Furthermore, we re-analyse the experimental data of electron-induced ionization cross-sections on a pure thick Si target in the energy range from 3 to 25 keV, which were originally obtained by Zhu
et al
. who used a regularization method. The reprocessed cross-sections obtained in this study exhibit good agreement with the reported results within the error range. To the best of our knowledge, this is the first experimental report of the
K
-shell ionization cross-sections of Si from positron impact. |
|---|---|
| AbstractList | In this study, a neural network method is proposed for solving the inverse problem in the measurement of inner-shell ionization cross-sections using the thick-target method. It was applied to calculate the
K
-shell ionization cross-section of silicon (Si) from positron impacts in the energy range from 4.5 to 9 keV, using a Monte Carlo simulation program called PENELOPE to construct a comprehensive characteristic X-ray yield and cross-section database, serving as a foundation for training the neural network. The experimental values are compared with those obtained using regularization, yield differential, and distorted-wave Born approximation (DWBA) theoretical models. Our findings reveal that the cross-section results obtained from all three algorithms are in good agreement with the theoretical DWBA values within the error range. Moreover, our study highlights the superiority of the neural network algorithm in solving ill-posed problems, compared with traditional regularization algorithms and the yield differential method. Furthermore, we re-analyse the experimental data of electron-induced ionization cross-sections on a pure thick Si target in the energy range from 3 to 25 keV, which were originally obtained by Zhu
et al
. who used a regularization method. The reprocessed cross-sections obtained in this study exhibit good agreement with the reported results within the error range. To the best of our knowledge, this is the first experimental report of the
K
-shell ionization cross-sections of Si from positron impact. In this study, a neural network method is proposed for solving the inverse problem in the measurement of inner-shell ionization cross-sections using the thick-target method. It was applied to calculate the K-shell ionization cross-section of silicon (Si) from positron impacts in the energy range from 4.5 to 9 keV, using a Monte Carlo simulation program called PENELOPE to construct a comprehensive characteristic X-ray yield and cross-section database, serving as a foundation for training the neural network. The experimental values are compared with those obtained using regularization, yield differential, and distorted-wave Born approximation (DWBA) theoretical models. Our findings reveal that the cross-section results obtained from all three algorithms are in good agreement with the theoretical DWBA values within the error range. Moreover, our study highlights the superiority of the neural network algorithm in solving ill-posed problems, compared with traditional regularization algorithms and the yield differential method. Furthermore, we re-analyse the experimental data of electron-induced ionization cross-sections on a pure thick Si target in the energy range from 3 to 25 keV, which were originally obtained by Zhu et al. who used a regularization method. The reprocessed cross-sections obtained in this study exhibit good agreement with the reported results within the error range. To the best of our knowledge, this is the first experimental report of the K-shell ionization cross-sections of Si from positron impact. |
| Author | Li, Y. D. Liu, Z. H. Pan, M. Huang, C. J. Wu, Y. |
| Author_xml | – sequence: 1 givenname: Y. D. surname: Li fullname: Li, Y. D. organization: North China Electric Power University Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, School of Nuclear Science and Engineering, - Beijing 102206, China – sequence: 2 givenname: Y. surname: Wu fullname: Wu, Y. organization: North China Electric Power University Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, School of Nuclear Science and Engineering, - Beijing 102206, China – sequence: 3 givenname: C. J. surname: Huang fullname: Huang, C. J. organization: North China Electric Power University Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, School of Nuclear Science and Engineering, - Beijing 102206, China – sequence: 4 givenname: Z. H. surname: Liu fullname: Liu, Z. H. organization: North China Electric Power University Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, School of Nuclear Science and Engineering, - Beijing 102206, China – sequence: 5 givenname: M. surname: Pan fullname: Pan, M. organization: North China Electric Power University Beijing Key Laboratory of Passive Safety Technology for Nuclear Energy, School of Nuclear Science and Engineering, - Beijing 102206, China |
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| SubjectTerms | Accuracy Algorithms Born approximation Characteristic X rays Electrons Error analysis Ill posed problems Inverse problems Ionization cross sections Monte Carlo simulation Neural networks Positrons Regularization Regularization methods Silicon |
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| Title | Neural-network–based algorithm for the inverse problem of measuring K-shell ionization cross-sections of Si induced by 3–25 keV electrons and 4.5–9 keV positrons using the thick-target method |
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