Precision MARS Mass Reconstruction of A2744: Synergizing the Largest Strong-lensing and Densest Weak-lensing Data Sets from JWSTopen access
- Authors
- CHA, SANGJUN; Hyeonghan Kim; Scofield Zachary P.; Joo Hyungjin; Jee M. James
- Issue Date
- Feb-2024
- Publisher
- University of Chicago Press
- Citation
- Astrophysical Journal, v.961, no.2
- Journal Title
- Astrophysical Journal
- Volume
- 961
- Number
- 2
- URI
- https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/22938
- DOI
- 10.3847/1538-4357/ad0cbf
- ISSN
- 0004-637X
1538-4357
- Abstract
- We present a new high-resolution free-form mass model of A2744 that combines both weak-lensing (WL) and strong-lensing (SL) data sets from JWST. The SL data set comprises 286 multiple images, presenting the most extensive SL constraint to date for a single cluster. The WL data set, employing photo-z selection, yields a source density of similar to 350arcmin-2, marking the densest WL constraint ever. The combined mass reconstruction enables the highest-resolution mass map of A2744 within the similar to 1.8 Mpc <bold>x</bold> 1.8 Mpc reconstruction region to date, revealing an isosceles triangular structure with two legs of similar to 1 Mpc and a base of similar to 0.6 Mpc. Although our algorithm, which is called MAximum-entropy ReconStruction (MARS), is entirely blind to the cluster galaxy distribution, the resulting mass reconstruction traces the brightest cluster galaxies remarkably well. The five strongest mass peaks coincide with the five most luminous cluster galaxies within less than or similar to 2''. We do not detect any unusual mass peaks that are not traced by the cluster galaxies, unlike the findings in previous studies. Our mass model shows the smallest scatter of SL multiple images in both source (similar to 005) and image (similar to 01) planes, which is lower than in previous studies by a factor of similar to 4. Although MARS represents the mass field with an extremely large number of free parameters (similar to 300,000), it converges to a solution within a few hours because we use a deep-learning technique. We make our mass and magnification maps publicly available.
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Collections - The Graduate School > 대학원 지구천문대기학부 > 1. Journal Articles

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