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Adaptive semi-linear inversion of strong gravitational lens imaging

Nightingale, J.W.; Dye, S.

Adaptive semi-linear inversion of strong gravitational lens imaging Thumbnail


J.W. Nightingale

Associate Professor


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.


Nightingale, J., & Dye, S. (2015). Adaptive semi-linear inversion of strong gravitational lens imaging. Monthly Notices of the Royal Astronomical Society, 452(3),

Journal Article Type Article
Acceptance Date Jun 29, 2015
Publication Date Jul 29, 2015
Deposit Date May 2, 2017
Publicly Available Date May 2, 2017
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
Volume 452
Issue 3
Keywords methods: observational – galaxies: evolution – galaxies: structure
Public URL
Publisher URL
Additional Information This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2015 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.


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