Fig. 5: Exploring the parameter landscape with Bayesian optimization.
From: Optimal 3D chemical imaging with multimodal electron tomography
a Illustration of Bayesian optimization for fused multimodal tomography simulations. Model certainty shown by the surface’s varying edge width; red points represent assessed reconstructions. b The weights between elastic and inelastic modalities (λ1 and λ2 respectively) change with the number of projections but not substantially. The yellow circle on each plot corresponds to a target MM-ET experiment with a HAADF projection SNR of 10 and chemical projection SNR of 5. c 3D visualization of the ground truth CoO/NiO nanotube. Scale cube, 15 nm. d Bayesian optimization searching for the cost function weighting that minimizes NRMSE. Each black dot indicates a full MM-ET reconstruction using 141 HAADF, 11 chemical projections. e The three cost function components show smooth asymptotic convergence. Multimodal tomography—as with all iterative tomographic reconstruction methods—should be assessed for proper convergence.
