Unmixed metals: variations in the enrichment of <i>z</i> ∼ 4 sub-damped Lyman α systems
Bibliographic record
Abstract
ABSTRACT The chemical abundance patterns of near-pristine objects provide important constraints on the properties of the first generations of stars in the Universe. We present the chemical abundances of five very metal-poor ([M/H]$< -2.5$) sub-damped Ly$\alpha$ systems (subDLAs) covering the redshift range $3.6< z< 4.3$, identified with the XQ-100 survey. We find that the subDLAs in our sample show consistent chemical abundance patterns (in particular [C/O], [Al/O], and [Fe/O]) with those of very metal-poor DLAs. Based on Voigt profile fitting, the chemical abundance ratios [C/O], [Al/O], and [Si/O] of individual velocity components in at least three of the subDLAs show some intrinsic scatter. In order to verify these chemical inhomogeneities in absorption components, we present a novel method for computing ionization corrections (ICs) on a component-by-component basis and show that ICs alone cannot explain the variations in [C/O], [Al/O], and [Si/O] between components of the same absorber at $\approx 2\sigma$ significance. Comparing the observed abundance ratios to the simulated core-collapse supernovae yields of early stellar populations, we find that all individual components of the subDLAs appears to be enriched by progenitor masses of $\lesssim 30$ M$_\odot$. The observed inhomogeneities between components can be reproduced by differences in the progenitor mass or supernova explosion energy. As such, the observed chemical inhomogeneities between components can be explained by poorly mixed gas from different nucleosynthetic events.
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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 | 0.000 |
| 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) | 0.001 | 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".