COSMOS PHOTOMETRIC REDSHIFTS WITH 30-BANDS FOR 2-deg<sup>2</sup>
Bibliographic record
Abstract
We present accurate photometric redshifts (photo-z) in the 2-deg 2 COSMOS field. The redshifts are computed with 30 broad, intermediate, and narrowbands covering the UV ( Galaxy Evolution Explorer ), visible near-IR (NIR; Subaru, Canada–France–Hawaii Telescope (CFHT), United Kingdom Infrared Telescope, and National Optical Astronomy Observatory), and mid-IR ( Spitzer /IRAC). A χ 2 template-fitting method ( Le Phare ) was used and calibrated with large spectroscopic samples from the Very Large Telescope Visible Multi-Object Spectrograph and the Keck Deep Extragalactic Imaging Multi-Object Spectrograph. We develop and implement a new method which accounts for the contributions from emission lines ([O ii ], Hβ, Hα, and Lyα) to the spectral energy distributions (SEDs). The treatment of emission lines improves the photo-z accuracy by a factor of 2.5. Comparison of the derived photo-z with 4148 spectroscopic redshifts (i.e., Δ z = z s − z p ) indicates a dispersion of at i + AB < 22.5, a factor of 2–6 times more accurate than earlier photo-z in the COSMOS, CFHT Legacy Survey, and the Classifying Object by Medium-Band Observations-17 survey fields. At fainter magnitudes i + AB < 24 and z < 1.25, the accuracy is . The deep NIR and Infrared Array Camera coverage enables the photo-z to be extended to z ∼ 2, albeit with a lower accuracy ( at i + AB ∼ 24). The redshift distribution of large magnitude-selected samples is derived and the median redshift is found to range from z m = 0.66 at 22 < i + AB < 22.5 to z m = 1.06 at 24.5 < i + AB < 25. At i + AB < 26.0, the multiwavelength COSMOS catalog includes approximately 607,617 objects. The COSMOS-30 photo-z enables the full exploitation of this survey for studies of galaxy and large-scale structure evolution at high redshift.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".