Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models

• URL: http://arxiv.org/abs/2511.04792v1
• Date: Thu, 06 Nov 2025 20:23:41 GMT
• Title: Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
• Authors: Gabriel Missael Barco, Ronan Legin, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur,
• Abstract summary: In strong gravitational lensing, score-based models can serve as expressive, data-driven priors for scientific inverse problems.<n>We demonstrate the first successful demonstration of joint source-and-lens inference with a score-based prior.
• Score: 4.99431898057919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Score-based models can serve as expressive, data-driven priors for scientific inverse problems. In strong gravitational lensing, they enable posterior inference of a background galaxy from its distorted, multiply-imaged observation. Previous work, however, assumes that the lens mass distribution (and thus the forward operator) is known. We relax this assumption by jointly inferring the source and a parametric lens-mass profile, using a sampler based on GibbsDDRM but operating in continuous time. The resulting reconstructions yield residuals consistent with the observational noise, and the marginal posteriors of the lens parameters recover true values without systematic bias. To our knowledge, this is the first successful demonstration of joint source-and-lens inference with a score-based prior.

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