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 erg 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 0.000 |
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 teacher head, 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".