Model-Independent Mass Reconstruction of the Hubble Frontier Field Clusters with MARS Based on Self-Consistent Strong Lensing Data

Abstract

We present new strong-lensing (SL) mass reconstruction of the six Hubble Frontier Fields (HFF) clusters with the MAximum-entropy ReconStruction ($\mathtt{MARS}$) algorithm. $\mathtt{MARS}$ 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 dataset 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 $\mathtt{MARS}$ never uses the galaxy distributions as priors. Our study corroborates the flexibility and accuracy of the $\mathtt{MARS}$ algorithm and demonstrates that $\mathtt{MARS}$ is a powerful tool in the JWST era, when $2-3$ times larger number of multiple image candidates become available for SL mass reconstruction, and self-consistency within the dataset becomes a critical issue.

19 pages, 9 figures, 7 tables, submitted to ApJ

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