Traversing the Star-forming Main Sequence with Molecular Gas Stacks of <i>z</i> ∼ 1.6 Cluster Galaxies
Bibliographic record
Abstract
Abstract The cluster environment has been shown to affect the molecular gas content of cluster members, yet a complete understanding of this often subtle effect has been hindered due to a lack of detections over the full parameter space of galaxy star formation rates (SFRs) and stellar masses. Here, we stack CO(2–1) spectra of z ∼ 1.6 cluster galaxies to explore the average molecular gas fractions of galaxies both at lower mass (log(M */M ⊙) ∼ 9.6) and further below the star-forming main sequence (SFMS; ΔMS ∼ −0.9) than other literature studies; this translates to a 3σ gas mass limit of ∼7 × 109 M ⊙ for stacked galaxies below the SFMS. We divide our sample of 54 z ∼ 1.6 cluster galaxies, derived from the Spitzer Adaptation of the Red-Sequence Cluster Survey, into nine groupings, for which we recover detections in 8. The average gas content of the full cluster galaxy population is similar to coeval field galaxies matched in stellar mass and SFR. However, when further split by CO-undetected and CO-detected, we find that galaxies below the SFMS have statistically different gas fractions from the field scaling relations, spanning deficiencies to enhancements from 2σ below to 3σ above the expected field gas fractions, respectively. These differences between z = 1.6 cluster and field galaxies below the SFMS are likely due to environmental processes, though further investigation of spatially resolved properties and more robust field scaling relation calibration in this parameter space are required.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".