The Baryonic Collapse Efficiency of Galaxy Groups in the RESOLVE and ECO Surveys
Bibliographic record
Abstract
We examine the z = 0 group-integrated stellar and cold baryonic (stars + cold atomic gas) mass functions (group SMF and CBMF) and the baryonic collapse efficiency (group cold baryonic to dark matter halo mass ratio) using the RESOLVE and ECO survey galaxy group catalogs and a galform semi-analytic model (SAM) mock catalog. The group SMF and CBMF fall off more steeply at high masses and rise with a shallower low-mass slope than the theoretical halo mass function (HMF). The transition occurs at the group-integrated cold baryonic mass Mᶜᵒˡᵈ* ∼10¹¹ M⨀ . The SAM, however, has significantly fewer groups at the transition mass ∼10¹¹ M⨀ and a steeper low-mass slope than the data, suggesting that feedback is too weak in low-mass halos and conversely too strong near the transition mass. Using literature prescriptions to include hot halo gas and potential unobservable galaxy gas produces a group BMF with a slope similar to the HMF even below the transition mass. Its normalization is lower by a factor of ∼2, in agreement with estimates of warm-hot gas making up the remaining difference. We compute baryonic collapse efficiency with the halo mass calculated two ways, via halo abundance matching (HAM) and via dynamics (extended all the way to three-galaxy groups using stacking). Using HAM, we find that baryonic collapse efficiencies reach a flat maximum for groups across the halo mass range of Mₕₐₗₒ ∼10¹¹.⁴⁻¹² M⨀, which we label “nascent groups.” Using dynamics, however, we find greater scatter in baryonic collapse efficiencies, likely indicating variation in group hot-to-cold baryon ratios. Similarly, we see higher scatter in baryonic collapse efficiencies in the SAM when using its true groups and their group halo masses as opposed to friends-of-friends groups and HAM masses. *= subscript bary
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".