CANUCS: An Updated Mass and Magnification Model of A370 with JWST
Bibliographic record
Abstract
Abstract We present an updated mass and magnification model of galaxy cluster A370 using new NIRCam, NIRISS, and NIRSpec data from the Canadian NIRISS Unbiased Cluster Survey (CANUCS). Using Lenstool and a combination of archival Hubble Space Telescope (HST) and MUSE data with new JWST data as constraints, we derive an improved gravitational lensing model and extract magnifications of background galaxies with uncertainties. Using our best fit model, we perform a search for new multiply imaged systems via predicted positions. We report no new multiply imaged systems with identifiable redshifts, likely due to already very deep HST and Spitzer data but confirm a z ∼ 8 multiply imaged system by measuring its redshift with NIRISS and NIRSpec spectra. We find that the overall shape of the critical curve for a source at z = 9.0 is similar to previous models of A370, with small changes. We investigate the z ∼ 8 galaxy with two images observable with an apparent magnitude in the F125W band of 26.0 ± 0.2 and 25.6 ± 0.1. After correcting for the magnifications of the images, 7.4 − 0.3 + 0.5 and 9.4 − 0.4 + 0.5 , we use spectral energy distribution fitting to find an intrinsic stellar mass of log(M */M ⊙) = 7.49 − 0.05 + 0.04 , intrinsic star formation rate of 2.8 − 0.3 + 0.4 M ⊙ yr−1, and M UV of −21.3 − 0.2 + 0.2 , which is close to the knee of the luminosity function at that redshift. Our model, and corresponding magnification, shear, and convergence maps are available on request and will be made publicly available on MAST in a CANUCS data release (doi:10.17909/ph4n-6n76).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".