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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".