http://arxiv.org/abs/2201.10076
In this work we present relensing, a package written in python whose goal is to model galaxy clusters from gravitational lensing. With relensing we extent the amount of software available, which provides the scientific community with a wide range of models that help to compare and therefore validate the physical results that rely on them. We implement a free-form approach which computes the gravitational deflection potential on a adaptive irregular grid, from which one can characterize the cluster and its properties as a gravitational lens. Here, we use two alternative penalty functions to constrain strong lensing. We apply relensing to two toy models, in order to explore under which conditions one can get a better performance in the reconstruction. We find that by applying a smoothing to the deflection potential enhances the capability of this approach to recover the shape and size of the galaxy cluster’s mass profile, as well as its magnification map, which translates in a better estimation of the critical and caustic curves. The power that the smoothing provides is also tested on the simulated clusters Ares and Hera, for which our results represent an improvement with respect to reconstructions that were carried out with methods of the same nature than relensing. At the same time, the smoothing also increases the stability of our implementation, and decreases the computation time. On its current state, relensing is available upon request.
D. Torres-Ballesteros and L. Castañeda
Wed, 26 Jan 22
28/53
Comments: Comments are welcome
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