astro-informatics/QuantifAI
PyTorch-based radio-interferometric imaging reconstruction package with scalable Bayesian uncertainty quantification relying on data-driven (learned) priors
This tool helps radio astronomers reconstruct high-quality images from raw radio interferometric observations. It takes the raw visibility data from radio telescopes and produces reconstructed images of celestial objects, complete with reliable estimates of uncertainty for each pixel. Radio astronomers and astrophysicists who analyze complex radio data would use this.
No commits in the last 6 months.
Use this if you need to create detailed radio astronomical images and accurately quantify the uncertainty in your observations without computationally intensive methods like MCMC sampling.
Not ideal if you are working outside of radio interferometry or require a solution that does not leverage data-driven prior information for image reconstruction.
Stars
12
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Language
Jupyter Notebook
License
GPL-3.0
Last pushed
Feb 17, 2025
Commits (30d)
0
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