Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917067219795968 |
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| author | Barco, Gabriel Missael Legin, Ronan Stone, Connor Hezaveh, Yashar Perreault-Levasseur, Laurence |
| author_facet | Barco, Gabriel Missael Legin, Ronan Stone, Connor Hezaveh, Yashar Perreault-Levasseur, Laurence |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models Barco, Gabriel Missael Legin, Ronan Stone, Connor Hezaveh, Yashar Perreault-Levasseur, Laurence Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning 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. |
| title | Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models |
| topic | Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2511.04792 |