Entropy-regularized Maximum-Likelihood cluster mass reconstruction

Abstract

We present a new method for reconstructing two-dimensional mass maps of galaxy clusters from the image distortion of background galaxies. In contrast to most previous approaches, which directly convert locally averaged image ellipticities to mass maps (direct methods), our entropy-regularized maximum-likelihood method is an inverse approach. Albeit somewhat more expensive computationally, our method allows high spatial resolution in those parts of the cluster where the lensing signal is strong enough. Furthermore, it allows to straightforwardly incorporate additional constraints, such as magnification information or strong-lensing features. Using synthetic data, we compare our new approach to direct methods and find indeed a substantial improvement especially in the reconstruction of mass peaks. The main differences to previously published inverse methods are discussed.