MAximum-entropy ReconStruction (MARS): A New Strong-lensing Reconstruction Algorithm for the JWST Era

Abstract

AbstractThe MAximum-entropy ReconStruction (MARS) method is a free-form strong-lensing (SL) reconstruction algorithm, which adopts the maximum cross-entropy as a regularization. MARS shows remarkable convergence of multiple images in both source (∼0.”02) and image planes (∼0.”05 – 0.”1) while suppressing spurious fluctuations. Although the reconstruction requires a large number of free parameters exceeding ∼19,000, our implementation through PyTorch can obtain the reconstruction within hours. From our test using the publicly available synthetic clusters, we have verified that the reconstructed radial mass profiles are consistent with the truth within 1 percent. This makes MARS one of the best-performing SL reconstruction methods. We apply MARS to the six Hubble Frontier Fields clusters and present new mass reconstruction results. We also reconstruct a mass model of Abell 2744 using both weak-lensing (WL) and SL data from the JWST observations, with the largest dataset of Abell 2744, including 286 SL multiple images and ∼350 arcmin−2 WL constraints.