<i>HERSCHEL</i>EXPLOITATION OF LOCAL GALAXY ANDROMEDA (HELGA). III. THE STAR FORMATION LAW IN M31
Bibliographic record
Abstract
We present a detailed study of how the star formation rate (SFR) relates to the interstellar medium (ISM) of M31 at ∼140 pc scales. The SFR is calculated using the far-ultraviolet and 24 μm emission, corrected for the old stellar population in M31. We find a global value for the SFR of and compare this with the SFR found using the total far-infrared luminosity. There is general agreement in regions where young stars dominate the dust heating. Atomic hydrogen (H i ) and molecular gas (traced by carbon monoxide, CO) or the dust mass is used to trace the total gas in the ISM. We show that the global surface densities of SFR and gas mass place M31 among a set of low-SFR galaxies in the plot of Kennicutt. The relationship between SFR and gas surface density is tested in six radial annuli across M31, assuming a power law relationship with index, N . The star formation (SF) law using total gas traced by H i and CO gives a global index of N = 2.03 ± 0.04, with a significant variation with radius; the highest values are observed in the 10 kpc ring. We suggest that this slope is due to H i turning molecular at Σ Gas ∼ 10 M ☉ pc −2 . When looking at H 2 regions, we measure a higher mean SFR suggesting a better spatial correlation between H 2 and SF. We find N ∼ 0.6 with consistent results throughout the disk—this is at the low end of values found in previous work and argues against a superlinear SF law on small scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.027 |
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".