SDSS-IV MaNGA: How the Stellar Populations of Passive Central Galaxies Depend on Stellar and Halo Mass
Bibliographic record
Abstract
Abstract We analyze spatially resolved and co-added SDSS-IV MaNGA spectra with signal-to-noise ratio ∼100 from 2200 passive central galaxies (z ∼ 0.05) to understand how central galaxy assembly depends on stellar mass (M *) and halo mass (M h ). We control for systematic errors in M h by employing a new group catalog from Tinker and the widely used Yang et al. catalog. At fixed M *, the strengths of several stellar absorption features vary systematically with M h . Completely model-free, this is one of the first indications that the stellar populations of centrals with identical M * are affected by the properties of their host halos. To interpret these variations, we applied full spectral fitting with the code alf. At fixed M *, centrals in more massive halos are older, show lower [Fe/H], and have higher [Mg/Fe] with 3.5σ confidence. We conclude that halos not only dictate how much M * galaxies assemble but also modulate their chemical enrichment histories. Turning to our analysis at fixed M h , high-M * centrals are older, show lower [Fe/H], and have higher [Mg/Fe] for M h > 1012 h −1 M ⊙ with confidence >4σ. While massive passive galaxies are thought to form early and rapidly, our results are among the first to distinguish these trends at fixed M h . They suggest that high-M * centrals experienced unique early formation histories, either through enhanced collapse and gas fueling or because their halos were early forming and highly concentrated, a possible signal of galaxy assembly bias.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".