PYRA and MYTHRA: new statistical analysis for image reconstruction
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.
Versions
Related
- Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction
- Image Fidelity is Not Field Fidelity: Joint Thermodynamic Reconstruction and Error Localization in Neural Tomography
- A New AI/ML Based Image Reconstruction Technique for Hard X-ray Coded Aperture Imagers
- Bayesian Image Reconstruction with Spatially Variant PSFs in X-ray Astronomy
- GRAVITY-RESOLVE: A comprehensive image reconstruction tool for Galactic Center observations with GRAVITY
BibTeX
@misc{drevon2026pyra,
title = {{PYRA and MYTHRA: new statistical analysis for image reconstruction}},
author = {Julien Drevon and Margaux Abello and Florentin Millour and Anthony Meilland and Armando Domiciano de Souza},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.15365},
eprint = {2609.15365},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.15365},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - PYRA and MYTHRA: new statistical analysis for image reconstruction
AU - Julien Drevon
AU - Margaux Abello
AU - Florentin Millour
AU - Anthony Meilland
AU - Armando Domiciano de Souza
PY - 2026
DA - 2026/09/14
PB - arXiv
DO - 10.48550/arxiv.2609.15365
UR - https://arxiv.org/abs/2609.15365
AB - 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.
ER -