Model-independent Mass Reconstruction of the Hubble Frontier Field Clusters with MARS Based on Self-consistent Strong-lensing Data

Abstract

Abstract We present a new strong-lensing (SL) mass reconstruction of the six Hubble Frontier Fields (HFF) clusters with the MAximum-entropy ReconStruction (MARS) algorithm. MARS is a new free-form inversion method, which suppresses spurious small-scale fluctuations while achieving excellent convergence in positions of multiple images. For each HFF cluster, we obtain a model-independent mass distribution from the compilation of the self-consistent SL data in the literature. With 100–200 multiple images per cluster, we reconstruct solutions with small scatters of multiple images in both source (∼0.″02) and image planes (0.″05–0.″1), which are lower than the previous results by a factor of 5–10. An outstanding case is the MACS J0416.1-2403 mass reconstruction, which is based on the largest high-quality SL data set where all 236 multiple images/knots have spectroscopic redshifts. Although our solution is smooth on a large scale, it reveals group/galaxy-scale peaks where the substructures are required by the data. We find that in general, these mass peaks are in excellent spatial agreement with the member galaxies, although MARS never uses the galaxy distributions as priors. Our study corroborates the flexibility and accuracy of the MARS algorithm and demonstrates that MARS is a powerful tool in the JWST era, when a 2–3 times larger number of multiple image candidates become available for SL mass reconstruction, and self-consistency within the data set becomes a critical issue.