CF-HiZELS, an ∼10 deg2 emission-line survey with spectroscopic follow-up: Hα, [O iii] + Hβ and [O ii] 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 the wide-field infrared camera/Canada-France-Hawaii Telescope and the lowOH2 filter (1.187 ± 0.005 μm) to survey ≈10 deg2 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(Z) 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 Hα emitters at z = 0.8, 1343 [O III] + Hβ emitters at z = 1.4 and 572 [O II] emitters at z = 2.2. We probe comoving volumes of >106 Mpc3 and find significant overdensities, including an 8.5σ (spectroscopically confirmed) overdensity of Hα emitters at z = 0.81. We derive Hα, [O III] + Hβ and [O II] 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 subdivide the samples in thousands of randomized 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 per cent) if they are based on volumes >5 × 105 Mpc3; errors on L* and φ* due to sample (cosmic) variance on surveys probing ∼104 and ∼105 Mpc3 are typically very high: ∼300 and ∼40-60 per cent, respectively.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.007 | 0.003 |
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".