Red Misfits in the Sloan Digital Sky Survey: properties of star-forming red galaxies
Bibliographic record
Abstract
We study Red Misfits, a population of red, star-forming galaxies in the local Universe. We classify galaxies based on inclination-corrected optical colours and specific star formation rates derived from the Sloan Digital Sky Survey Data Release 7. Although the majority of blue galaxies are star-forming and most red galaxies exhibit little to no ongoing star formation, a small but significant population of galaxies (∼11 per cent at all stellar masses) are classified as red in colour yet actively star-forming. We explore a number of properties of these galaxies and demonstrate that Red Misfits are not simply dusty or highly inclined blue cloud galaxies or quiescent red galaxies with poorly constrained star formation. The proportion of Red Misfits is nearly independent of environment, and this population exhibits both intermediate morphologies and an enhanced likelihood of hosting an active galactic nucleus. We conclude that Red Misfits are a transition population, gradually quenching on their way to the red sequence and this quenching is dominated by internal processes rather than environmentally driven processes. We discuss the connection between Red Misfits and other transition galaxy populations, namely S0s, red spirals, and green valley galaxies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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".