CANUCS: Constraining the MACS J0416.1-2403 strong lensing model with JWST NIRISS, NIRSpec, and NIRCam
Bibliographic record
Abstract
Context. Strong gravitational lensing in galaxy clusters has become an essential tool in astrophysics, as it allows one to directly probe the dark matter distribution and study magnified background sources. The precision and reliability of strong lensing models rely heavily on the number and quality of multiple images of background sources with spectroscopic redshifts. Aims. We present an updated strong lensing model of the galaxy cluster MACS J0416.1-2403 with the largest sample of multiple images with spectroscopic redshifts in a galaxy cluster field to date. Furthermore, we aim to demonstrate the effectiveness of JWST, particularly its NIRISS camera, for strong lensing studies. Methods. We used JWST’s NIRCam imaging and NIRSpec and NIRISS spectroscopy from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). The cluster mass model was constrained using Lenstool software. Results. Our new dataset, which we used for constraining the lens model, comprises 303 secure multiple images with spectroscopic redshifts from 111 background sources and includes 95 systems with previously known MUSE redshift and 16 systems (with 46 multiple images) for which we obtained spectroscopic redshift for the first time using NIRISS and NIRSpec spectroscopy. Three of the spectroscopic systems were not identified by previous JWST studies. The total number of secure spectroscopic systems is more than 20% higher than in the previous strong lensing studies of this cluster. The derived strong lensing model can reproduce multiple images with the root-mean-square distance of ∼0″.52. We also provide a full catalogue with 415 multiple images, including less reliable candidates. In total, we provide 15 new multiple-image system candidates (with 38 multiple images) not reported in previous studies. Furthermore, we demonstrate the effectiveness of JWST, particularly NIRISS, for obtaining spectroscopic redshifts of multiple images. As NIRISS F115W, F150W, and F200W grism spectroscopy captures at least two of the [O II] λ3727, [O III] λλ4959, 5007, and Hα lines at 1 ≲ z ≲ 3 (a redshift range particularly relevant for strong lensing studies) without target pre-selection, it complements MUSE and NIRSpec observations extremely well.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".