Near-infrared Survey and Photometric Redshifts in the Extended GOODS-North Field
Bibliographic record
Abstract
Abstract We present deep J- and H-band images in the extended Great Observatories Origins Deep Survey-North field covering an area of 0.22 deg2. The observations were taken using WIRCam on the 3.6 m Canada–France–Hawaii Telescope. Together with the reprocessed K s-band image, the 5σ limiting AB magnitudes (in 2″ diameter apertures) are 24.7, 24.2, and 24.4 AB mag in the J, H, and K s bands, respectively. We also release a multiband photometry and photometric redshift catalog containing 93,598 sources. For non-X-ray sources, we obtained a photometric redshift accuracy σ NMAD = 0.036 with an outlier fraction η = 7.3%. For X-ray sources, which are mainly active galactic nuclei (AGNs), we cross-matched our catalog with the updated 2M-CDFN X-ray catalog from Xue et al. and found that 658 out of 683 X-ray sources have counterparts. GALEX UV data are included in the photometric redshift computation for the X-ray sources to give σ NMAD = 0.040 with η = 10.5%. Our approach yields more accurate photometric redshift estimates compared to previous works in this field. In particular, by adopting AGN–galaxy hybrid templates, our approach delivers photometric redshifts for the X-ray counterparts with fewer outliers compared to the 3D-Hubble Space Telescope catalog, which fit these sources with galaxy-only templates.
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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 | 0.001 |
| 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) | 0.001 | 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".