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As established in previous papers of this series, observables in highlydistorted and magnified multiple images caused by the strong gravitationallensing effect can be used to constrain the distorting properties of thegravitational lens at the image positions. If the background source is extendedand contains substructure, like star forming regions, which is resolved inmultiple images, all substructure that can be matched across a minimum of threemultiple images can be used to infer the local distorting properties of thelens. In this work, we replace the manual feature selection by an automatedfeature extraction based on SExtractor for Python and show its superiorperformance. Despite its aimed development to improve our lens reconstruction,it can be employed in any other approach, as well. Valuable insights on thedefinition of an `image position' in the presence of noise are gained from ourcalibration tests. Applying it to observations of a five-image configuration ingalaxy cluster CL0024 and the triple-image configuration containing Hamilton'sobject, we determine local lens properties for multiple wavebands separately.Within current confidence bounds, all of them are consistent with each other,corroborating the wavelength-independence of strong lensing and offering a toolto detect deviations caused by micro-lensing and dust in further examples.

monthly notices of the royal astronomical society

Generalised model-independent characterisation of strong gravitational  lenses VIII. automated multi-band feature detection to constrain local lens  properties