SDSS-IV MaNGA: Stellar M/L gradients and the M/L-colour relation in galaxies
Bibliographic record
Abstract
ABSTRACT The stellar mass-to-light ratio gradient in SDSS r-band ∇(M*/Lr) of a galaxy depends on its mass assembly history, which is imprinted in its morphology and gradients of age, metallicity, and stellar initial mass function (IMF). Taking a MaNGA sample of 2051 galaxies with stellar masses ranging from 109 to 1012M⊙ released in SDSS DR15, we focus on face-on galaxies, without merger and bar signatures, and investigate the dependence of the 2D ∇(M*/Lr) on other galaxy properties, including M*/Lr-colour relationships by assuming a fixed Salpeter IMF as the mass normalization reference. The median gradient is ∇M*/Lr ∼ −0.1 (i.e. the M*/Lr is larger at the centre) for massive galaxies, becomes flat around M* ∼ 1010M⊙ and change sign to ∇M*/Lr ∼ 0.1 at the lowest masses. The M*/Lr inside a half-light radius increases with increasing galaxy stellar mass; in each mass bin, early-type galaxies have the highest value, while pure-disc late-type galaxies have the smallest. Correlation analyses suggest that the mass-weighted stellar age is the dominant parameter influencing the M*/Lr profile, since a luminosity-weighted age is easily affected by star formation when the specific star formation rate (sSFR) inside the half-light radius is higher than 10−3 Gyr−1. With increased sSFR gradient, one can obtain a steeper negative ∇(M*/Lr). The scatter in the slopes of M*/L-colour relations increases with increasing sSFR, for example, the slope for post-starburst galaxies can be flattened to 0.45 from the global value 0.87 in the M*/L versus g − r diagram. Hence converting galaxy colours to M*/L should be done carefully, especially for those galaxies with young luminosity-weighted stellar ages, which can have quite different star formation histories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".