High Molecular-gas to Dust Mass Ratios Predicted in Most Quiescent Galaxies
Bibliographic record
Abstract
Abstract Observations of cold molecular gas reservoirs are critical for understanding the shutdown of star formation in massive galaxies. While dust continuum is an efficient and affordable tracer, this method relies upon the assumption of a “normal” molecular-gas to dust mass ratio, δ GDR, typically of order 100. Recent null detections of quiescent galaxies in deep dust continuum observations support a picture where the cold gas and dust have been rapidly depleted or expelled. In this work, we present another viable explanation: a significant fraction of galaxies with low star formation per unit stellar mass are predicted to have extreme δ GDR ratios. We show that simulated massive quiescent galaxies at 0 < z < 3 in the simba cosmological simulations have δ GDR values that extend >4 orders of magnitude. The dust in most simulated quiescent galaxies is destroyed significantly more rapidly than the molecular gas depletes, and cannot be replenished. The transition from star-forming to quiescent halts dust formation via star formation processes, with dust subsequently destroyed by supernova shocks and thermal sputtering of dust grains embedded in hot plasma. After this point, the dust growth rate in the models is not sufficient to overcome the loss of >3 orders of magnitude in dust mass to return to normal values of δ GDR despite having high metallicity. Our results indicate that it is not straight forward to use a single observational indicator to robustly preselect exotic versus normal ratios. These simulations make strong predictions that can be tested with millimeter facilities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".