Photometric redshifts for the CFHTLS T0004 deep and wide fields
Bibliographic record
Abstract
<i>Aims. <i/>We compute photometric redshifts in the fourth public release of the Canada-France-Hawaii Telescope Legacy Survey. This unique multi-colour catalogue comprises photometry in four deep fields of 1 deg<sup>2<sup/> each and 35 deg<sup>2<sup/> distributed over three wide fields.<i>Methods. <i/>We used a template-fitting method to compute photometric redshifts calibrated with a large catalogue of 16 983 high-quality spectroscopic redshifts from the VVDS-F02, VVDS-F22, DEEP2, and the zCOSMOS surveys. The method includes correction of systematic offsets, template adaptation, and the use of priors. We also separated stars from galaxies using both size and colour information. <i>Results. <i/>Comparing with galaxy spectroscopic redshifts, we find a photometric redshift dispersion, , of 0.028–0.30 and an outlier rate, , of 3–4% in the deep field at < 24. In the wide fields, we find a dispersion of 0.037–0.039 and an outlier rate of 3–4% at < 22.5. Beyond = 22.5 in the wide fields the number of outliers rises from 5% to 10% at < 23 and < 24, respectively. For the wide sample the systematic redshift bias stays below 1% to < 22.5, whereas we find no significant bias in the deep fields. We investigated the effect of tile-to-tile photometric variations and demonstrated that the accuracy of our photometric redshifts is reduced by at most 21%. Application of our star-galaxy classifier reduced the contamination by stars in our catalogues from 60% to 8% at < 22.5 in our field with the highest stellar density while keeping a complete galaxy sample. Our CFHTLS T0004 photometric redshifts are distributed to the community. Our release includes 592891 ( < 22.5) and 244701 ( < 24) reliable galaxy photometric redshifts in the wide and deep fields, respectively.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".