A single galaxy population? Statistical evidence that the star-forming main sequence might be the tip of the iceberg
Bibliographic record
Abstract
ABSTRACT According to their specific star formation rate (sSFR), galaxies are often divided into ‘star-forming’ and ‘passive’ populations. It is argued that the former define a narrow ‘main sequence of star-forming galaxies’ (MSSF) of the form sSFR(M*), whereas ‘passive’ galaxies feature negligible levels of star formation activity. Here we use data from the Sloan Digital Sky Survey and the Galaxy And Mass Assembly survey at z < 0.1 to constrain the conditional probability of the sSFR at a given stellar mass. We show that the whole population of galaxies in the local Universe is consistent with a simple probability distribution with only one maximum (roughly corresponding to the MSSF) and relatively shallow power-law tails that fully account for the ‘passive’ population. We compare the quality of the fits provided by such unimodal ansatz against those coming from a double lognormal fit (illustrating the bimodal paradigm), finding that both descriptions are roughly equally compatible with the current data. In addition, we study the physical interpretation of the bidimensional distribution across the M*–sSFR plane and discuss potential implications from a theoretical and observational point of view. We also investigate correlations with metallicity, morphology, and environment, highlighting the need to consider at least an additional parameter in order to fully specify the physical state of a galaxy.
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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 | 0.003 | 0.012 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".