The CFHQSIR survey: a <i>Y</i>-band extension of the CFHTLS-Wide survey
Bibliographic record
Abstract
Context. The Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) has been conducted over a 5-yr period at the CFHT with the MegaCam instrument, totaling 450 nights of observations. The Wide Synoptic Survey is one component of the CFHTLS, covering 155 square degrees in four patches of 23 to 65 square degrees through the whole MegaCam filter set (u*, g’, r’, i’, z’) down to i’AB = 24.5. Aims. With the motivation of searching for high-redshift quasars at redshifts above 6.5, we extend the multi-wavelength CFHTLS-Wide data in the Y -band down to magnitudes of ~22.5 for point sources (5σ). Methods. We observed the four CFHTLS-Wide fields (except one quarter of the W3 field) in the Y -band with the Wide-field InfraRed Camera (WIRCam) at the CFHT. Each field was visited twice, at least three weeks apart. Each visit consisted of two dithered exposures. The images are reduced with the Elixir software used for the CFHTLS and modified to account for the properties of near-InfraRed (IR) data. Two series of image stacks are subsequently produced: four-image stacks for each WIRCam pointing, and one-square-degree tiles matched to the format of the CFHTLS data release. Photometric calibration is performed on stars by fitting stellar spectra to their CFHTLS photometric data and extrapolating their Y -band magnitudes. Results. After corrections accounting for correlated noise, we measure a limiting magnitude of YAB ≃ 22.4 for point sources (5σ) in an aperture diameter of 0.′′93, over 130 square degrees. We produce a multi-wavelength catalogue combining the CFHTLS-Wide optical data with our CFHQSIR (Canada–France High-z quasar survey in the near-InfraRed) Y -band data. We derive the Y -band number counts and compare them to the Vista Deep Extragalactic Observations survey (VIDEO). We find that the addition of the CFHQSIR Y -band data to the CFHTLS optical data increases the accuracy of photometric redshifts and reduces the outlier rate from 13.8% to 8.8% in the redshift range 1.05 ≲ z ≲ 1.2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".