The FENIKS Survey: Multiwavelength Photometric Catalog in the UDS Field, and Catalogs of Photometric Redshifts and Stellar Population Properties
Bibliographic record
Abstract
Abstract We present the construction of a deep multiwavelength point-spread-function-matched photometric catalog in the Ultra-Deep Survey (UDS) field following the final UKIDSS UDS release. The catalog includes photometry in 24 filters, from the MegaCam-uS 0.38 μm band to the Spitzer-IRAC 8 μm band, over ∼0.9 deg2 and with a 5σ depth of 25.3 AB in the K-band detection image. The catalog, containing ≈188,564 (136,235) galaxies at 0.2 < z < 8.0 with stellar mass log ( M * / M ⊙ ) > 8 and K-band total magnitude K < 25.2 (24.3) AB, enables a range of extragalactic studies. We also provide photometric redshifts, corresponding redshift probability distributions, and rest-frame absolute magnitudes and colors derived using the template-fitting code eazy-py. Photometric redshift errors are less than 3%−4% at z < 4 across the full brightness range in the K band and stellar mass range 8 < log ( M * / M ⊙ ) < 12 . Stellar population properties (e.g., stellar mass, star formation rate, dust extinction) are derived from the modeling of the spectral energy distributions using the codes FAST and Dense Basis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".