H-ATLAS/GAMA: quantifying the morphological evolution of the galaxy population using cosmic calorimetry
Bibliographic record
Abstract
Using results from the Herschel Astrophysical Terrahertz Large-Area Survey (H-ATLAS) and the Galaxy and Mass Assembly (GAMA) project, we show that, for galaxy masses above ≃ 108 M⊙, 51 per cent of the stellar mass-density in the local Universe is in early-type galaxies (ETGs; Sérsic n > 2.5) while 89 per cent of the rate of production of stellar mass-density is occurring in late-type galaxies (LTGs; Sérsic n < 2.5). From this zero-redshift benchmark, we have used a calorimetric technique to quantify the importance of the morphological transformation of galaxies over the history of the Universe. The extragalactic background radiation contains all the energy generated by nuclear fusion in stars since the big bang. By resolving this background radiation into individual galaxies using the deepest far-infrared survey with the Herschel Space Observatory and a deep near-infrared/optical survey with the Hubble Space Telescope (HST), and using measurements of the Sérsic index of these galaxies derived from the HST images, we estimate that ≃83 per cent of the stellar mass-density formed over the history of the Universe occurred in LTGs. The difference between this value and the fraction of the stellar mass-density that is in LTGs today implies there must have been a major transformation of LTGs into ETGs after the formation of most of the stars.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".