The H<b>α</b>luminosity function and star formation rate up to<b>z ∼ 1</b>
Bibliographic record
Abstract
We describe ISAAC/ESO-VLT observations of the Hαλ6563 Balmer line of 33 field galaxies from the Canada—France Redshift Survey (CFRS) with redshifts selected between 0.5 and 1.1. We detect Hα in emission in 30 galaxies and compare the properties of this sample with the low-redshift sample of CFRS galaxies at z∼ 0.2. We find that the Hα luminosity, L(Hα), is tightly correlated to M(BAB) in the same way for both the low- and high-redshift samples. L(Hα) is also correlated to L([O ii]λ3727), and again the relation appears to be similar at low and high redshifts. The ratio L(lsqb;O ii])/L(Hα) decreases for brighter galaxies by as much as a factor of 2 on average. Derived from the Hα luminosity function, the comoving Hα luminosity density increases by a factor 12 from 〈z〉= 0.2 to 〈z〉= 1.3. Our results confirm a strong rise of the star formation rate (SFR) at z < 1.3, proportional to (1 +z)4.1±0.3 (with H0 = 50 km s−1 Mpc−1, q0 = 0.5). We find an average SFR(2800 Å)/SFR (Hα) ratio of 3.2 using the Kennicutt SFR transformations. This corresponds to the dust correction that is required to make the near-ultraviolet data consistent with the reddening-corrected Hα data within the self-contained, I-selected CFRS sample.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".