THE NEXT GENERATION VIRGO CLUSTER SURVEY. XV. THE PHOTOMETRIC REDSHIFT ESTIMATION FOR BACKGROUND SOURCES
Bibliographic record
Abstract
The Next Generation Virgo Cluster Survey (NGVS) is an optical imaging survey covering 104 deg 2 centered on the Virgo cluster. Currently, the complete survey area has been observed in the u * giz bands and one third in the r band. We present the photometric redshift estimation for the NGVS background sources. After a dedicated data reduction, we perform accurate photometry, with special attention to precise color measurements through point-spread function homogenization. We then estimate the photometric redshifts with the Le Phare and BPZ codes. We add a new prior that extends to i AB = 12.5 mag. When using the u * griz bands, our photometric redshifts for 15.5 mag ⩽ i ≲ 23 mag or z phot ≲ 1 galaxies have a bias |Δ z | < 0.02, less than 5% outliers, a scatter σ outl.rej. , and an individual error on z phot that increases with magnitude (from 0.02 to 0.05 and from 0.03 to 0.10, respectively). When using the u * giz bands over the same magnitude and redshift range, the lack of the r band increases the uncertainties in the 0.3 ≲ z phot ≲ 0.8 range (−0.05 < Δ z < −0.02, σ outl.rej ∼ 0.06, 10%–15% outliers, and z phot.err. ∼ 0.15). We also present a joint analysis of the photometric redshift accuracy as a function of redshift and magnitude. We assess the quality of our photometric redshifts by comparison to spectroscopic samples and by verifying that the angular auto- and cross-correlation function w (θ) of the entire NGVS photometric redshift sample across redshift bins is in agreement with the expectations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".