Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines
Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass density in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the inf...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/accf84 |
| _version_ | 1797698829789691904 |
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| author | Alexandre Adam Laurence Perreault-Levasseur Yashar Hezaveh Max Welling |
| author_facet | Alexandre Adam Laurence Perreault-Levasseur Yashar Hezaveh Max Welling |
| author_sort | Alexandre Adam |
| collection | DOAJ |
| description | Modeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass density in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation. |
| first_indexed | 2024-03-12T03:59:41Z |
| format | Article |
| id | doaj.art-6adfdbc7e2c2484caafbaa41b317a3bf |
| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-03-12T03:59:41Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-6adfdbc7e2c2484caafbaa41b317a3bf2023-09-03T11:42:19ZengIOP PublishingThe Astrophysical Journal1538-43572023-01-019511610.3847/1538-4357/accf84Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference MachinesAlexandre Adam0https://orcid.org/0000-0001-8806-7936Laurence Perreault-Levasseur1https://orcid.org/0000-0003-3544-3939Yashar Hezaveh2https://orcid.org/0000-0002-8669-5733Max Welling3Department of Physics, Université de Montréal , Montréal, Canada ; alexandre.adam@umontreal.ca; Mila—Quebec Artificial Intelligence Institute , Montréal, Canada; Ciela—Montreal Institute for Astrophysical Data Analysis and Machine Learning , Montréal, CanadaDepartment of Physics, Université de Montréal , Montréal, Canada ; alexandre.adam@umontreal.ca; Mila—Quebec Artificial Intelligence Institute , Montréal, Canada; Ciela—Montreal Institute for Astrophysical Data Analysis and Machine Learning , Montréal, Canada; Center for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USADepartment of Physics, Université de Montréal , Montréal, Canada ; alexandre.adam@umontreal.ca; Mila—Quebec Artificial Intelligence Institute , Montréal, Canada; Ciela—Montreal Institute for Astrophysical Data Analysis and Machine Learning , Montréal, Canada; Center for Computational Astrophysics, Flatiron Institute , 162 5th Avenue, New York, NY 10010, USAMicrosoft Research AI4Science , Amsterdam, NetherlandsModeling strong gravitational lenses in order to quantify distortions in the images of background sources and to reconstruct the mass density in foreground lenses has been a difficult computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. In this work, we use a neural network based on the recurrent inference machine to reconstruct simultaneously an undistorted image of the background source and the lens mass density distribution as pixelated maps. The method iteratively reconstructs the model parameters (the image of the source and a pixelated density map) by learning the process of optimizing the likelihood given the data using the physical model (a ray-tracing simulation), regularized by a prior implicitly learned by the neural network through its training data. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions, which we demonstrate by using realistic lensing galaxies taken from the IllustrisTNG cosmological hydrodynamic simulation.https://doi.org/10.3847/1538-4357/accf84Convolutional neural networksAstronomical simulationsNonparametric inference |
| spellingShingle | Alexandre Adam Laurence Perreault-Levasseur Yashar Hezaveh Max Welling Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines The Astrophysical Journal Convolutional neural networks Astronomical simulations Nonparametric inference |
| title | Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines |
| title_full | Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines |
| title_fullStr | Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines |
| title_full_unstemmed | Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines |
| title_short | Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems Using Recurrent Inference Machines |
| title_sort | pixelated reconstruction of foreground density and background surface brightness in gravitational lensing systems using recurrent inference machines |
| topic | Convolutional neural networks Astronomical simulations Nonparametric inference |
| url | https://doi.org/10.3847/1538-4357/accf84 |
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