A Strong-lensing Model for the WMDF JWST/GTO Very Rich Cluster A1489
Bibliographic record
Abstract
Abstract We present a first strong-lensing model for the galaxy cluster RM J121218.5+273255.1 (z = 0.35; hereafter RMJ1212; also known as A1489). This cluster is among the top 0.1% richest clusters in the redMaPPer catalog; it is significantly detected in X-ray and through the Sunyaev–Zel’dovich effect in ROSAT and Planck data, respectively, and its optical luminosity distribution implies a very large lens, following mass-to-light scaling relations. Based on these properties it was chosen for the Webb Medium Deep Fields (WMDF) James Webb Space Telescope/Guaranteed Time Observations program. In preparation for this program, RMJ1212 was recently imaged with Gemini Multi-Object Spectrograph (GMOS) on Gemini North and in seven optical and near-infrared bands with the Hubble Space Telescope (HST). We use these data to map the inner mass distribution of the cluster, uncovering various sets of multiple images. We also search for high-redshift candidates in the data, as well as for transient sources. We find over a dozen high-redshift (z ≳ 6) candidates based on both photometric redshift and the dropout technique. No prominent (≳5σ) transients were found in the data between the two HST visits. Our lensing analysis reveals a relatively large lens with an effective Einstein radius of θ E ≃ 32 ± 3″ (z s = 2), in broad agreement with the scaling-relation expectations. RMJ1212 demonstrates that powerful lensing clusters can be selected in a robust and automated way following the light-traces-mass assumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".