Adaptive semi-linear inversion of strong gravitational lens imaging
We present a new pixelized method for the inversion of gravitationally lensed extended source images which we term adaptive semi-linear inversion (SLI). At the heart of the method is an h-means clustering algorithm which is used to derive a source plane pixelization that adapts to the lens model mag...
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| Format: | Article |
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Oxford University Press
2015
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| Online Access: | https://eprints.nottingham.ac.uk/42436/ |
| _version_ | 1848796486245023744 |
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| author | Nightingale, J.W. Dye, S. |
| author_facet | Nightingale, J.W. Dye, S. |
| author_sort | Nightingale, J.W. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We present a new pixelized method for the inversion of gravitationally lensed extended source images which we term adaptive semi-linear inversion (SLI). At the heart of the method is an h-means clustering algorithm which is used to derive a source plane pixelization that adapts to the lens model magnification. The distinguishing feature of adaptive SLI is that every pixelization is derived from a random initialization, ensuring that data discretization is performed in a completely different and unique way for every lens model parameter set. We compare standard SLI on a fixed source pixel grid with the new method and demonstrate the shortcomings of the former when modelling singular power-law ellipsoid (SPLE) lens profiles. In particular, we demonstrate the superior reliability and efficiency of adaptive SLI which, by design, fixes the number of degrees of freedom (NDOF) of the optimization and thereby removes biases present with other methods that allow the NDOF to vary. In addition, we highlight the importance of data discretization in pixel-based inversion methods, showing that adaptive SLI averages over significant systematics that are present when a fixed source pixel grid is used. In the case of the SPLE lens profile, we show how the method successfully samples its highly degenerate posterior probability distribution function with a single nonlinear search. The robustness of adaptive SLI provides a firm foundation for the development of a strong lens modelling pipeline, which will become necessary in the short-term future to cope with the increasing rate of discovery of new strong lens systems. |
| first_indexed | 2025-11-14T19:48:45Z |
| format | Article |
| id | nottingham-42436 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:48:45Z |
| publishDate | 2015 |
| publisher | Oxford University Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-424362020-05-04T17:12:16Z https://eprints.nottingham.ac.uk/42436/ Adaptive semi-linear inversion of strong gravitational lens imaging Nightingale, J.W. Dye, S. We present a new pixelized method for the inversion of gravitationally lensed extended source images which we term adaptive semi-linear inversion (SLI). At the heart of the method is an h-means clustering algorithm which is used to derive a source plane pixelization that adapts to the lens model magnification. The distinguishing feature of adaptive SLI is that every pixelization is derived from a random initialization, ensuring that data discretization is performed in a completely different and unique way for every lens model parameter set. We compare standard SLI on a fixed source pixel grid with the new method and demonstrate the shortcomings of the former when modelling singular power-law ellipsoid (SPLE) lens profiles. In particular, we demonstrate the superior reliability and efficiency of adaptive SLI which, by design, fixes the number of degrees of freedom (NDOF) of the optimization and thereby removes biases present with other methods that allow the NDOF to vary. In addition, we highlight the importance of data discretization in pixel-based inversion methods, showing that adaptive SLI averages over significant systematics that are present when a fixed source pixel grid is used. In the case of the SPLE lens profile, we show how the method successfully samples its highly degenerate posterior probability distribution function with a single nonlinear search. The robustness of adaptive SLI provides a firm foundation for the development of a strong lens modelling pipeline, which will become necessary in the short-term future to cope with the increasing rate of discovery of new strong lens systems. Oxford University Press 2015-07-29 Article PeerReviewed Nightingale, J.W. and Dye, S. (2015) Adaptive semi-linear inversion of strong gravitational lens imaging. Monthly Notices of the Royal Astronomical Society, 452 (3). pp. 2940-2959. ISSN 1365-2966 methods: observational – galaxies: evolution – galaxies: structure https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stv1455 doi:10.1093/mnras/stv1455 doi:10.1093/mnras/stv1455 |
| spellingShingle | methods: observational – galaxies: evolution – galaxies: structure Nightingale, J.W. Dye, S. Adaptive semi-linear inversion of strong gravitational lens imaging |
| title | Adaptive semi-linear inversion of strong gravitational lens imaging |
| title_full | Adaptive semi-linear inversion of strong gravitational lens imaging |
| title_fullStr | Adaptive semi-linear inversion of strong gravitational lens imaging |
| title_full_unstemmed | Adaptive semi-linear inversion of strong gravitational lens imaging |
| title_short | Adaptive semi-linear inversion of strong gravitational lens imaging |
| title_sort | adaptive semi-linear inversion of strong gravitational lens imaging |
| topic | methods: observational – galaxies: evolution – galaxies: structure |
| url | https://eprints.nottingham.ac.uk/42436/ https://eprints.nottingham.ac.uk/42436/ https://eprints.nottingham.ac.uk/42436/ |