The neutral hydrogen cosmological mass density at<i>z</i>= 5
Bibliographic record
Abstract
We present the largest homogeneous survey of z > 4.4 damped Lyα systems (DLAs) using the spectra of 163 QSOs that comprise the Giant Gemini GMOS (GGG) survey. With this survey we make the most precise high-redshift measurement of the cosmological mass density of neutral hydrogen, |$\Omega_{\rm H\,{\small i}}$|. At such high redshift, important systematic uncertainties in the identification of DLAs are produced by strong intergalactic medium absorption and QSO continuum placement. These can cause spurious DLA detections, result in real DLAs being missed or bias the inferred DLA column density distribution. We correct for these effects using a combination of mock and higher resolution spectra, and show that for the GGG DLA sample the uncertainties introduced are smaller than the statistical errors on |$\Omega_{\rm H\,{\small i}}$|. We find |$\rm \Omega _{\rm H\,\small {I}}=0.98^{+0.20}_{-0.18}\times 10^{-3}$| at 〈z〉 = 4.9, assuming a 20 per cent contribution from lower column density systems below the DLA threshold. By comparing to literature measurements at lower redshifts, we show that |$\Omega_{\rm H\,{\small i}}$| can be described by the functional form |$\Omega _{\rm H\,\small {I}}(z)\propto (1+z)^{0.4}$|. This gradual decrease from z = 5 to 0 is consistent with the bulk of H i gas being a transitory phase fuelling star formation, which is continually replenished by more highly ionized gas from the intergalactic medium and from recycled galactic winds.
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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 | 0.001 | 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) | 0.002 | 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".