JINGLE – IV. Dust, H i gas, and metal scaling laws in the local Universe
Bibliographic record
Abstract
ABSTRACT Scaling laws of dust, H i gas, and metal mass with stellar mass, specific star formation rate, and metallicity are crucial to our understanding of the build-up of galaxies through their enrichment with metals and dust. In this work, we analyse how the dust and metal content varies with specific gas mass (MH i/M⋆) across a diverse sample of 423 nearby galaxies. The observed trends are interpreted with a set of Dust and Element evolUtion modelS (DEUS) – including stellar dust production, grain growth, and dust destruction – within a Bayesian framework to enable a rigorous search of the multidimensional parameter space. We find that these scaling laws for galaxies with −1.0 ≲ log MH i/M⋆ ≲ 0 can be reproduced using closed-box models with high fractions (37–89 ${{\ \rm per\ cent}}$) of supernova dust surviving a reverse shock, relatively low grain growth efficiencies (ϵ = 30–40), and long dust lifetimes (1–2 Gyr). The models have present-day dust masses with similar contributions from stellar sources (50–80 ${{\ \rm per\ cent}}$) and grain growth (20–50 ${{\ \rm per\ cent}}$). Over the entire lifetime of these galaxies, the contribution from stardust (>90 ${{\ \rm per\ cent}}$) outweighs the fraction of dust grown in the interstellar medium (<10 ${{\ \rm per\ cent}}$). Our results provide an alternative for the chemical evolution models that require extremely low supernova dust production efficiencies and short grain growth time-scales to reproduce local scaling laws, and could help solving the conundrum on whether or not grains can grow efficiently in the interstellar medium.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".