CANUCS/Technicolor Data Release 1: Imaging, Photometry, Slit Spectroscopy, and Stellar Population Parameters*
Bibliographic record
Abstract
Abstract We present the first data release of the Canadian NIRISS Unbiased Cluster Survey (CANUCS), a JWST Cycle 1 GTO program targeting five lensing clusters and flanking fields in parallel (A370, MACS0416, MACS0417, MACS1149, and MACS1423; survey area ∼100 arcmin 2 ), with NIRCam imaging, NIRISS slitless spectroscopy, and NIRSpec prism multiobject spectroscopy. Fields centered on cluster cores include imaging in eight bands from 0.9–4.4 μ m, alongside continuous NIRISS coverage from 1.15–2 μ m, while the NIRCam flanking fields provide five wide-band and nine medium-band filters for exceptional spectral sampling, all to ∼29 mag AB . We also present JWST in Technicolor, a Cycle 2 follow-up GO program targeting three CANUCS clusters (A370, MACS0416, and MACS1149). The Technicolor program adds NIRISS slitless spectroscopy in F090W to the cluster fields while adding eight wide-, medium-, and narrowband filters to the flanking fields. This provides NIRCam imaging in all wide- and medium-band filters over ∼30 arcmin 2 . This paper describes our data reduction and photometry methodology. We release NIRCam, NIRISS, and Hubble Space Telescope imaging, point-spread functions (PSFs), PSF-matched imaging, photometric catalogs, and photometric and spectroscopic redshifts. We provide lens models and stellar population parameters in up to 19 filters for ∼53,000 galaxies in the cluster fields, and ∼44,000 galaxies in up to 29 filters in the flanking fields. We further present 733 NIRSpec spectra and redshift measurements up to z = 10.8. Comparing against our photometric redshifts, we find catastrophic outlier rates of only 4%–7% and scatter of σ NMAD of 0.01–0.03.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".