Evidence of pre-processing and a dependence on dynamical state for low-mass satellite galaxies
Bibliographic record
Abstract
We study the dependence of satellite star formation rate and morphology on group dynamics for a sample of Sloan Digital Sky Survey groups. We classify the group dynamical state and study satellite properties for populations of galaxies at small and large group-centric radii. For galaxies at large radii we find no differences in the star-forming or disc fraction for those in Gaussian groups compared to those in non-Gaussian groups. By comparing the star-forming and disc fractions of infalling galaxies to field galaxies we find evidence for the pre-processing of both star formation rate and morphology. The strength of pre-processing increases with halo mass and is highest for low-mass galaxies infalling on to high-mass haloes. We show that the star formation rate of galaxies at small radii correlates with group dynamical state, with galaxies in non-Gaussian groups showing enhanced star-forming fractions compared to galaxies in Gaussian groups. Similar correlations are not seen for the disc fractions of galaxies at small radii. This seems to suggest that either the mechanisms driving star formation quenching at small halo-centric radii are more efficient in dynamically relaxed groups or that non-Gaussian groups have assembled more recently and therefore satellites of the groups will have been exposed to these transforming mechanisms for less time.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".