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

TL;DR

This paper introduces MARS, a new free-form strong lensing mass reconstruction method, applied to Hubble Frontier Fields clusters, achieving high accuracy and revealing substructures without prior galaxy distribution assumptions.

Contribution

The paper presents MARS, a novel mass reconstruction algorithm that suppresses artifacts and achieves high-precision, model-independent mass maps from strong lensing data.

Findings

Reconstructed mass distributions with small positional scatters.

Revealed galaxy-scale substructures aligned with member galaxies.

Demonstrated MARS's effectiveness on large, high-quality datasets.

Abstract

We present new strong-lensing (SL) mass reconstruction of the six Hubble Frontier Fields (HFF) clusters with the MAximum-entropy ReconStruction (${\tt MARS}$) algorithm. ${\tt MARS}$ 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 ${\tt MARS}$ never uses the galaxy distributions as priors. Our study corroborates the flexibility and accuracy of the ${\tt MARS}$ algorithm and demonstrates that ${\tt MARS}$ 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.