Strong-lensing source reconstruction with variationally optimized Gaussian processes Article Swipe
Konstantin Karchev
,
Adam Coogan
,
Christoph Weniger
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stac311
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stac311
Strong-lensing images provide a wealth of information both about the magnified source and about the dark matter distribution in the lens. Precision analyses of these images can be used to constrain the nature of dark matter. However, this requires high-fidelity image reconstructions and careful treatment of the uncertainties of both lens mass distribution and source light, which are typically difficult to quantify. In anticipation of future high-resolution data sets, in this work we leverage a range of recent developments in machine learning to develop a new Bayesian strong-lensing image analysis pipeline. Its highlights are (a) a fast, GPU-enabled, end-to-end differentiable strong-lensing image simulator; (b) a new, statistically principled source model based on a computationally highly efficient approximation to Gaussian processes that also takes into account pixellation; and (c) a scalable variational inference framework that enables simultaneously deriving posteriors for tens of thousands of lens and source parameters and optimizing hyperparameters via stochastic gradient descent. Besides efficient and accurate parameter estimation and lens model uncertainty quantification, the main aim of the pipeline is the generation of training data for targeted simulation-based inference of dark matter substructure, which we will exploit in a companion paper.
Related Topics
Concepts
Physics
Dark matter
Weak gravitational lensing
Leverage (statistics)
Strong gravitational lensing
Pipeline (software)
Lens (geology)
Computer science
Algorithm
Artificial intelligence
Optics
Galaxy
Astrophysics
Redshift
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/stac311
- https://academic.oup.com/mnras/article-pdf/512/1/661/42904428/stac311.pdf
- OA Status
- hybrid
- Cited By
- 12
- References
- 97
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4210584023
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4210584023Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/mnras/stac311Digital Object Identifier
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Strong-lensing source reconstruction with variationally optimized Gaussian processesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-02-03Full publication date if available
- Authors
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Konstantin Karchev, Adam Coogan, Christoph WenigerList of authors in order
- Landing page
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https://doi.org/10.1093/mnras/stac311Publisher landing page
- PDF URL
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https://academic.oup.com/mnras/article-pdf/512/1/661/42904428/stac311.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://academic.oup.com/mnras/article-pdf/512/1/661/42904428/stac311.pdfDirect OA link when available
- Concepts
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Physics, Dark matter, Weak gravitational lensing, Leverage (statistics), Strong gravitational lensing, Pipeline (software), Lens (geology), Computer science, Algorithm, Artificial intelligence, Optics, Galaxy, Astrophysics, Redshift, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2024: 6, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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