The Frontier Fields lens modelling comparison project


Gravitational lensing by clusters of galaxies offers a powerful probe of their structure and mass distribution. Several research groups have developed techniques independently to achieve this goal. While these methods have all provided remarkably high-precision mass maps, particularly with exquisite imaging data from the Hubble Space Telescope (HST), the reconstructions themselves have never been directly compared. In this paper, we present for the first time a detailed comparison of methodologies for fidelity, accuracy and precision. For this collaborative exercise, the lens modelling community was provided simulated cluster images that mimic the depth and resolution of the ongoing HST Frontier Fields. The results of the submitted reconstructions with the un-blinded true mass profile of these two clusters are presented here. Parametric, free-form and hybrid techniques have been deployed by the participating groups and we detail the strengths and trade-offs in accuracy and systematics that arise for each methodology. We note in conclusion that several properties of the lensing clusters are recovered equally well by most of the lensing techniques compared in this study. For example, the reconstruction of azimuthally averaged density and mass profiles by both parametric and freeform methods matches the input models at the level of ∼10 per cent. Parametric techniques are generally better at recovering the 2D maps of the convergence and of the magnification. For the best-performing algorithms, the accuracy in the magnification estimate is∼10 per cent at μtrue = 3 and it degrades to ∼30 per cent at μtrue ∼ 10.

Cite this paper

@inproceedings{Meneghetti2017TheFF, title={The Frontier Fields lens modelling comparison project}, author={Massimo Meneghetti and Priyamvada Natarajan and D. A. Coe and Elizabeth Contini and G. De Lucia and C. Giocoli and Alvaro Acebron and Stefano Borgani and Maru{\vs}a Brada{\vc} and Jose Maria Diego and Adam Hoag and Masahide Ishigaki and Tommy L. Johnson and Eric Jullo and Ryosuke Kawamata and D. Lam and Marceau Limousin and Jori Liesenborgs and Motohiro Oguri and Kenneth Sebesta and Keren Sharon and Liliya L. R. Williams and Adi Zitrin}, year={2017} }