DEEP CFHT Y-BAND IMAGING OF VVDS-F22 FIELD. I. DATA PRODUCTS AND PHOTOMETRIC REDSHIFTS
Bibliographic record
Abstract
ABSTRACT We present our deep Y-band imaging data of a 2 square degree field within the F22 region of the VIMOS VLT Deep Survey. The observations were conducted using the WIRCam instrument mounted at the Canada–France–Hawaii Telescope (CFHT). The total on-sky time was 9 hr, distributed uniformly over 18 tiles. The scientific goals of the project are to select faint quasar candidates at redshift <?CDATA $z\gt 2.2$?> and constrain the photometric redshifts for quasars and galaxies. In this paper, we present the observation and the image reduction, as well as the photometric redshifts that we derived by combining our Y-band data with the CFHTLenS <?CDATA ${u}^{* }g^{\prime} r^{\prime} i^{\prime} z^{\prime} $?> optical data and UKIDSS DXS JHK near-infrared data. With the J-band image as a reference, a total of ∼80,000 galaxies are detected in the final mosaic down to a Y-band <?CDATA $5\sigma $?> point-source limiting depth of 22.86 mag. Compared with the ∼3500 spectroscopic redshifts, our photometric redshifts for galaxies with <?CDATA $z\lt 1.5$?> and <?CDATA $i^{\prime} \lesssim 24.0$?> mag have a small systematic offset of <?CDATA $| {\rm{\Delta }}z| \lesssim 0.2$?> , 1σ scatter <?CDATA $0.03\lt {\sigma }_{{\rm{\Delta }}z}\lt 0.06$?> , and less than 4.0% of catastrophic failures. We also compare with the CFHTLenS photometric redshifts and find that ours are more reliable at <?CDATA $z\gtrsim 0.6$?> because of the inclusion of the near-infrared bands. In particular, including the Y-band data can improve the accuracy at <?CDATA $z\sim 1.0\mbox{--}2.0$?> because the location of the 4000 Å break is better constrained. The Y-band images, the multiband photometry catalog, and the photometric redshifts are released at http://astro.pku.edu.cn/astro/data/DYI.html .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".