PYRA and MYTHRA: new statistical analysis for image reconstruction
TL;DR
PYRA and MYTHRA are presented, two tools designed to improve the robustness and reproducibility of interferometric imaging and statistical analysis of large reconstruction ensembles provides a powerful framework for assessing image reliability and reducing reconstruction artifacts in optical interferometry.
Abstract
Image reconstruction in optical interferometry remains a challenging inverse problem due to sparse Fourier sampling and the strong dependence of the reconstructed images on user-defined parameters. We present PYRA (Python for MiRA) and MYTHRA (Mean Astrophysical Images with PYRA), two tools designed to improve the robustness and reproducibility of interferometric imaging. PYRA automatically generates large ensembles of MiRA reconstructions by exploring a wide range of reconstruction parameters, while MYTHRA statistically analyzes the resulting images to identify the most consistent solutions and derive a final averaged image with associated uncertainty estimates. The methodology is validated using the blind datasets of the 2024 Interferometric Imaging Contest, where our reconstruction strategy achieved the best overall score. We further apply the method to VLTI/MATISSE observations of the triple AGB system π1 Gru and the A[e] supergiant 3 Pup. These results demonstrate that statistical analysis of large reconstruction ensembles provides a powerful framework for assessing image reliability and reducing reconstruction artifacts in optical interferometry