Mesh-free free-form lensing – I. Methodology and application to mass reconstruction. Issue 3 (14th June 2016)

• Record Type: Journal Article
• Authors: Merten, Julian
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 461:Issue 3(2016)
• Page Start: 2328
• Page End: 2345
• Publication Date: 2016-06-14
• Subjects: gravitational lensing: strong -- gravitational lensing: weak -- methods: numerical -- galaxies: clusters: general -- dark matter -- large-scale structure of Universe; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stw1413
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract : Many applications and algorithms in the field of gravitational lensing make use of meshes with a finite number of nodes to analyse and manipulate data. Specific examples in lensing are astronomical CCD images in general, the reconstruction of density distributions from lensing data, lens–source plane mapping or the characterization and interpolation of a point spread function. We present a numerical framework to interpolate and differentiate in the mesh-free domain, defined by nodes with coordinates that follow no regular pattern. The framework is based on radial basis functions (RBFs) to smoothly represent data around the nodes. We demonstrate the performance of Gaussian RBF-based, mesh-free interpolation and differentiation, which reaches the sub-percent level in both cases. We use our newly developed framework to translate ideas of free-form mass reconstruction from lensing on to the mesh-free domain. By reconstructing a simulated mock lens we find that strong-lensing only reconstructions achieve <10 per cent accuracy in the areas where these constraints are available but provide poorer results when departing from these regions. Weak-lensing only reconstructions give <10 per cent accuracy outside the strong-lensing regime, but cannot resolve the inner core structure of the lens. Once both regimes are combined, accurate reconstructions can be achieved over the full field of view. The reconstruction of a simulated lens, using constraints that mimics real observations, yields accurate results in terms of surface-mass density, Navarro-Frenk-White profile (NFW) parameters, Einstein radius and magnification map recovery, encouraging the application of this method to real data.