ENTROPY-REGULARIZED MAXIMUM-LIKELIHOOD CLUSTER MASS RECONSTRUCTION

We present a new method for reconstructing two-dimensional mass maps o f 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 entrop y-regularized maximum-likelihood method is an inverse approach. Albeit somewhat more expensive computationally, our method allows high spati al resolution in those parts of the cluster where the lensing signal i s strong enough. Furthermore, it allows to straightforwardly incorpora te additional constraints, such as magnification information or strong -lensing features. Using synthetic data, we compare our new approach t o direct methods and find indeed a substantial improvement especially in the reconstruction of mass peaks. The main differences to previousl y published inverse methods are discussed.