Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry - Equipe Image, Modélisation, Analyse, GEométrie, Synthèse
Communication Dans Un Congrès Année : 2024

Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry

Résumé

The advent of next-generation radio interferometers like the Square Kilometer Array promises to revolutionise our radio astronomy observational capabilities. The unprecedented volume of data these devices generate requires fast and accurate image reconstruction algorithms to solve the ill-posed radio interferometric imaging problem. Most state-of-the-art reconstruction methods lack trustworthy and scalable uncertainty quantification, which is critical for the rigorous scientific interpretation of radio observations. We propose an unsupervised technique based on a conformalized version of a radio-augmented equivariant bootstrapping method, which allows us to quantify uncertainties for fast reconstruction methods. Noticeably, we rely on reconstructions from ultra-fast unrolled algorithms. The proposed method brings more reliable uncertainty estimations to our problem than existing alternatives.
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Dates et versions

hal-04767562 , version 1 (05-11-2024)

Identifiants

Citer

Mostafa Cherif, Tobías I. Liaudat, Jonathan Kern, Christophe Kervazo, Jérôme Bobin. Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry. NEURIPS 2024 ML4PS : Machine Learning and the Physical Sciences Workshop, Dec 2024, Vancouver, Canada. ⟨hal-04767562⟩
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