[2N07-10] Nuclear Data Evaluation, Processing, and Validation

[2N07] Prediction of optimal potential for nucleon-nucleus scattering using machine learning

*Shoto Watanabe¹, Futoshi Minato², Masaaki Kimura¹, Nobuyuki Iwamoto^2,1, Sota Yoshida³ (1. Hokkaido Univ., 2. JAEA, 3. Utsunomiya Univ.)

Keywords:Gaussian process regression, Optical model parameter, Coupled-channel optical model, Elastic scattering angular distribution, Parameter uncertainty

Using Gaussian process regression analysis, we have attempted to predict optical potentials that reproduce well the experimental data of nucleon-nucleus scattering at arbitrary incident energies. Using the optimal values of the parameters of the optical potential at each energy obtained using the method presented in the previous paper, we report the results of estimating the optimal values of the parameters at arbitrary energies by Gaussian process regression analysis.

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