Cosmic Star Formation History to<i>z</i> = 1 from a Narrow Emission Line-selected Tunable-Filter Survey
Bibliographic record
Abstract
We report the results of a deep three-dimensional imaging survey of the Hubble Deep Field North using the Taurus Tunable Filter at the William Herschel Telescope. This survey was designed to search for new line-emitting populations of objects missed by other techniques and to measure the cosmic star formation rate density from a line-selected survey. We observed in three contiguous sequences of narrowband slices in the 7100, 8100, and 9100 Å regions of the spectrum, corresponding to a cosmological volume of up to 1000 Mpc 3 at z = 1, down to a flux limit of ∼2 × 10 -17 ergs cm -2 s -1 . The survey is deep enough to be highly complete for low line luminosity galaxies. Cross-matching with existing spectroscopy in the field results in a small line-luminosity–limited sample, with very high redshift-identification completeness containing seven [O II], Hβ, and Hα emitters over the redshift range 0.3–0.9. Treating this as a direct star formation rate–selected sample, we estimate the star formation history of the universe to z = 1. We find no evidence for any new population of line-emitting objects contributing significantly to the cosmological star formation rate density. Rather, from our complete narrowband sample, we find that the star formation history is consistent with earlier estimates from broadband imaging surveys and other less deep line-selected surveys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".