LENSED: A code for the forward reconstruction of lenses and sources from strong lensing observations

Nicolas Tessore, Fabio Bellagamba, R. Benton Metcalf

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Robust modelling of strong lensing systems is fundamental to exploit the information they contain about the distribution of matter in galaxies and clusters. In this work, we present lensed, a new code which performs forward parametric modelling of strong lenses. lensed takes advantage of a massively parallel ray-tracing kernel to perform the necessary calculations on a modern graphics processing unit (GPU). This makes the precise rendering of the background lensed sources much faster, and allows the simultaneous optimization of tens of parameters for the selected model. With a single run, the code is able to obtain the full posterior probability distribution for the lens light, the mass distribution and the background source at the same time. lensed is first tested on mock images which reproduce realistic space-based observations of lensing systems. In this way, we show that it is able to recover unbiased estimates of the lens parameters, even when the sources do not follow exactly the assumed model. Then, we apply it to a subsample of the Sloan Lens ACS Survey lenses, in order to demonstrate its use on real data. The results generally agree with the literature, and highlight the flexibility and robustness of the algorithm.

    Original languageEnglish
    Pages (from-to)3115-3128
    Number of pages14
    JournalMonthly Notices of the Royal Astronomical Society
    Volume463
    Issue number3
    Early online date5 Sep 2016
    DOIs
    Publication statusPublished - Dec 2016

    Keywords

    • Data analysis
    • Gravitational lensing: strong-methods
    • Methods: numerical
    • Methods: statistical
    • Techniques: image processing

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