Size, shade, or shape? The contribution of galaxies of different types to the star formation history of the Universe from SDSS-IV MaNGA
Bibliographic record
Abstract
ABSTRACT By fitting stellar populations to the fourth generation of the Sloan Digital Sky Survey (SDSS-IV) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey observations of ∼7000 suitably weighted individual galaxies, we reconstruct the star formation history of the Universe, which we find to be in reasonable agreement with previous studies. Dividing the galaxies by their present-day stellar mass, we demonstrate the downsizing phenomenon, whereby the more massive galaxies hosted the most star formation at earlier times. Further dividing the galaxy sample by colour and morphology, we find that a galaxy’s present-day colour tells us more about its historical contribution to the cosmic star formation history than its current morphology. We show that downsizing effects are greatest among galaxies currently in the blue cloud, but that the level of downsizing in galaxies of different morphologies depends quite sensitively on the morphological classification used, due largely to the difficulty in classifying the smaller low-mass galaxies from their ground-based images. Nevertheless, we find agreement that among galaxies with stellar masses $M_{\star } \gt 6\times 10^{9}\, \mathrm{ M}_{\odot }$, downsizing is most significant in spirals. However, there are complicating factors. For example, for more massive galaxies, we find that colour and morphology are predictors of the past star formation over a longer time-scale than in less massive systems. Presumably this effect is reflecting the longer period of evolution required to alter these larger galaxies’ physical properties, but shows that conclusions based on any single property do not tell the full story.
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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 | 0.003 |
| 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 | 0.001 |
| 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".