Star formation laws in the Andromeda galaxy: gas, stars, metals and the surface density of star formation
Bibliographic record
Abstract
We use hierarchical Bayesian regression analysis to investigate star formation laws in the Andromeda galaxy (M31) in both local (30, 155 and 750 pc) and global cases. We study and compare the well-known Kennicutt–Schmidt law, the extended Schmidt law and the metallicity/star formation correlation. Using a combination of Hα and 24 μm emission, a combination of far-ultraviolet and 24 μm, and the total infrared emission, we estimate the total star formation rate (SFR) in M31 to be between 0.35 ± 0.04 and 0.4 ± 0.04 M⊙ yr−1. We produce a stellar mass surface density map using IRAC 3.6 μm emission and measured the total stellar mass to be 6.9 × 1010 M⊙. For the Kennicutt–Schmidt law in M31, we find the power-law index N to be between 0.49 and 1.18; for all the laws, the power-law index varies more with changing gas tracer than with SFR tracer. The power-law index also changes with distance from the centre of the galaxy. We also applied the commonly used ordinary least-squares fitting method and showed that using different fitting methods leads to different power-law indices. There is a correlation between the surface density of SFR and the stellar mass surface density, which confirms that the Kennicutt–Schmidt law needs to be extended to consider the other physical properties of galaxies. We found a weak correlation between metallicity, the SFR and the stellar mass surface density.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".