A 10 deg2 Lyman α survey at z=8.8 with spectroscopic follow-up: strong constraints on the luminosity function and implications for other surveys★
Bibliographic record
Abstract
Candidate galaxies at redshifts of z ∼ 10 are now being found in extremely deep surveys, probing very small areas. As a consequence, candidates are very faint, making spectroscopic confirmation practically impossible. In order to overcome such limitations, we have undertaken the CF-HiZELS survey, which is a large-area, medium-depth near-infrared narrow-band survey targeted at z = 8.8 Lyman α (Lyα) emitters (LAEs) and covering 10 deg2 in part of the SSA22 field with the Canada–France–Hawaii Telescope (CFHT). We surveyed a comoving volume of 4.7 × 106 Mpc3 to a Lyα luminosity limit of 6.3 × 1043舁erg舁s−1. We look for Lyα candidates by applying the following criteria: (i) clear emission-line source, (ii) no optical detections (ugriz from CFHTLS), (iii) no visible detection in the optical stack (ugriz > 27), (iv) visually checked reliable NBJ and J detections and (v) J − K ≤ 0. We compute photometric redshifts and remove a significant amount of dusty lower redshift line-emitters at z ∼ 1.4 or 2.2. A total of 13 Lyα candidates were found, of which two are marked as strong candidates, but the majority have very weak constraints on their spectral energy distributions. Using follow-up observations with SINFONI/VLT, we are able to exclude the most robust candidates as LAEs. We put a strong constraint on the Lyα luminosity function at z ∼ 9 and make realistic predictions for ongoing and future surveys. Our results show that surveys for the highest redshift LAEs are susceptible of multiple contaminations and that spectroscopic follow-up is absolutely necessary.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".