Sub-damped Lyman α systems in the XQ-100 survey – I. Identification and contribution to the cosmological H i budget
Bibliographic record
Abstract
Abstract Sub-damped Lyman α systems (subDLAs; H i column densities of 19.0 ≤ logN(H i) < 20.3) are rarely included in the cosmic H i census performed at redshifts zabs ≳ 1.5, yet are expected to contribute significantly to the overall H i mass budget of the Universe. In this paper, we present a blindly selected sample of 155 subDLAs found along 100 quasar sightlines (with a redshift path-length ΔX = 475) in the XQ-100 legacy survey to investigate the contribution of subDLAs to the H i mass density of the Universe. The impact of X-Shooter’s spectral resolution on Ly α absorber identification is evaluated, and found to be sufficient for reliably finding absorbers down to a column density of logN(H i) ≥ 18.9. We compared the implications of searching for subDLAs solely using H i absorption versus the use of metal lines to confirm the identification, and found that metal-selection techniques would have missed 75 subDLAs. Using a bootstrap Monte Carlo simulation, we computed the column density distribution function (f(N, X)) and the cosmological H i mass density ($\Omega _{\rm H\,{\small I}}$) of subDLAs and compared with our previous work based on the XQ-100 damped Lyman α systems. We do not find any significant redshift evolution in f(N, X) or $\Omega _{\rm H\,{\small I}}$ for subDLAs. However, subDLAs contribute 10–20 per cent of the total $\Omega _{\rm H\,{\small I}}$ measured at redshifts 2 < z < 5, and thus have a small but significant contribution to the H i budget of the Universe.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".