CF-HiZELS, a 10 deg$^2$ emission-line survey with spectroscopic follow-up: H\alpha, [OIII]+H\beta\ and [OII] luminosity functions at z=0.8, 1.4 and 2.2
Bibliographic record
Abstract
We present results from the largest contiguous narrow-band survey in the near-infrared. We have used WIRCam/CFHT and the lowOH2 filter (1.187$\pm$0.005 $μ$m) to survey ~10 deg$^2$ of contiguous extragalactic sky in the SA22 field. A total of ~6000 candidate emission-line galaxies are found. We use deep ugrizJK data to obtain robust photometric redshifts. We combine our data with the High-redshift Emission Line Survey (HiZELS), explore spectroscopic surveys (VVDS, VIPERS) and obtain our own spectroscopic follow-up with KMOS, FMOS and MOSFIRE to derive large samples of high-redshift emission-line selected galaxies: 3471 H$α$ emitters at z=0.8, 1343 [OIII]+H$β$ emitters at z=1.4 and 572 [OII] emitters at z=2.2. We probe co-moving volumes of >10$^6$ Mpc$^3$ and find significant over-densities, including an 8.5$σ$ (spectroscopically confirmed) over-density of H$α$ emitters at z=0.81. We derive H$α$, [OIII]+H$β$ and [OII] luminosity functions at z=0.8,1.4,2.2, respectively, and present implications for future surveys such as Euclid. Our uniquely large volumes/areas allow us to sub-divide the samples in thousands of randomised combinations of areas and provide a robust empirical measurement of sample/cosmic variance. We show that surveys for star-forming/emission-line galaxies at a depth similar to ours can only overcome cosmic-variance (errors <10%) if they are based on volumes >5x10$^{5}$ Mpc$^{3}$; errors on $L^*$ and $ϕ^*$ due to sample (cosmic) variance on surveys probing ~10$^4$ Mpc$^{3}$ and ~10$^5$ Mpc$^{3}$ are typically very high: ~300% and ~40-60%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.004 |
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".