ALMA Reveals Diverse Dust-to-gas Mass Ratios and Quenching Modes in Old Quiescent Galaxies
Bibliographic record
Abstract
Abstract Recent discoveries of dust and molecular gas in quiescent galaxies (QGs) up to z ∼ 4 challenge the long-standing view that the interstellar medium depletes rapidly once star formation ceases, raising key questions of whether dust and gas coevolve in QGs, and how their depletion links to stellar aging. We present deep Atacama Large Millimeter/submillimeter Array Band 6 continuum and CO(3–2) observations of 17 QGs at z ∼ 0.4 in the COSMOS field. Using the dust-to-molecular gas mass ratio ( δ DGR ) as a key diagnostic, we trace postquenching evolution of the cold interstellar medium. Our study triples the number of QGs with direct δ DGR estimates, constraining 12 systems with stellar population ages of ∼5–10 Gyr. For the first time, we show that δ DGR in QGs ranges from ∼8× below to ∼2.5× above the canonical value of δ DGR ∼ 1/100. Despite uniformly low molecular gas fractions (median f H 2 = M H 2 / M ⋆ ∼ 4.1 % ), QGs follow diverse evolutionary paths: about half exhibit rapid (∼700 Myr) exponential dust decline with age, while the rest show mild decline over ≳2 Gyr, maintaining elevated δ DGR ≳ 1/100. Our results support simulations' predictions of dust and molecular gas evolving independently postquenching, without a preferred quenching mode. This challenges the use of dust continuum as a H 2 tracer, implying that quenching cannot be robustly linked to interstellar medium conditions when relying solely on dust or gas.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".