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Robust modelling of strong lensing systems is fundamental to exploit theinformation they contain about the distribution of matter in galaxies andclusters. In this work, we present Lensed, a new code which performs forwardparametric modelling of strong lenses. Lensed takes advantage of a massivelyparallel ray-tracing kernel to perform the necessary calculations on a moderngraphics processing unit (GPU). This makes the precise rendering of thebackground lensed sources much faster, and allows the simultaneous optimisationof tens of parameters for the selected model. With a single run, the code isable to obtain the full posterior probability distribution for the lens light,the mass distribution and the background source at the same time. Lensed isfirst tested on mock images which reproduce realistic space-based observationsof lensing systems. In this way, we show that it is able to recover unbiasedestimates of the lens parameters, even when the sources do not follow exactlythe assumed model. Then, we apply it to a subsample of the SLACS lenses, inorder to demonstrate its use on real data. The results generally agree with theliterature, and highlight the flexibility and robustness of the algorithm.

Lensed: a code for the forward reconstruction of lenses and sources from strong lensing observations