Bayesian atomic structure calculations for collisional problems

Synopsis The calculations of collisional processes require an accurate description of the target. In general, the atomic structure is obtained through tedious iterations in which a variety of configurations and parameters are chosen to minimize the differences between the numerical and experimental...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-01, Vol.1412 (13), p.132027
Main Authors: Mendez, AMP, Filippo, J I Di, Lopez, S D, Mitnik, D M
Format: Article
Language:English
Subjects:
Atomic structure
Bayesian analysis
Beryllium
Machine learning
Oscillator strengths
Physics
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Summary:Synopsis The calculations of collisional processes require an accurate description of the target. In general, the atomic structure is obtained through tedious iterations in which a variety of configurations and parameters are chosen to minimize the differences between the numerical and experimental values of the energies and the oscillator strengths. Using a Bayesian machine learning analysis through a Tree-structured Parzen Estimator, we can reproduce the experimental atomic structure with high accuracy. Results for neutral beryllium are presented.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1412/13/132027