Bayesian atomic structure calculations for collisional problems

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SynopsisThe 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.