Now You See It, Now You Don’t: Star Formation Truncation Precedes the Loss of Molecular Gas by ∼100 Myr in Massive Poststarburst Galaxies at z ∼ 0.6
Bibliographic record
Abstract
Abstract We use ALMA observations of CO(2–1) in 13 massive ( M * ≳ 10 11 M ⊙ ) poststarburst galaxies at z ∼ 0.6 to constrain the molecular gas content in galaxies shortly after they quench their major star-forming episode. The poststarburst galaxies in this study are selected from the Sloan Digital Sky Survey spectroscopic samples (Data Release 14) based on their spectral shapes, as part of the Studying QUenching at Intermediate-z Galaxies: Gas, angu L → ar momentum, and Evolution ( SQuIGG L ⃗ E ) program. Early results showed that two poststarburst galaxies host large H 2 reservoirs despite their low inferred star formation rates (SFRs). Here we expand this analysis to a larger statistical sample of 13 galaxies. Six of the primary targets (45%) are detected, with M H 2 ≳ 10 9 M ⊙ . Given their high stellar masses, this mass limit corresponds to an average gas fraction of 〈 f H 2 ≡ M H 2 / M * 〉 ∼ 7 % or ∼14% using lower stellar masses estimates derived from analytic, exponentially declining star formation histories. The gas fraction correlates with the D n 4000 spectral index, suggesting that the cold gas reservoirs decrease with time since burst, as found in local K+A galaxies. Star formation histories derived from flexible stellar population synthesis modeling support this empirical finding: galaxies that quenched ≲150 Myr prior to observation host detectable CO(2–1) emission, while older poststarburst galaxies are undetected. The large H 2 reservoirs and low SFRs in the sample imply that the quenching of star formation precedes the disappearance of the cold gas reservoirs. However, within the following 100–200 Myr, the SQuIGG L ⃗ E galaxies require the additional and efficient heating or removal of cold gas to bring their low SFRs in line with standard H 2 scaling relations.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".