The ALMaQUEST Survey – V. The non-universality of kpc-scale star formation relations and the factors that drive them
Bibliographic record
Abstract
ABSTRACT Using a sample of ∼15 000 kpc-scale star-forming spaxels in 28 galaxies drawn from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we investigate the galaxy-to-galaxy variation of the ‘resolved’ Schmidt–Kennicutt relation (rSK; $\Sigma _{\rm H_2}$–ΣSFR), the ‘resolved’ star-forming main sequence (rSFMS; Σ⋆–ΣSFR), and the ‘resolved’ molecular gas main sequence (rMGMS; Σ⋆–$\Sigma _{\rm H_2}$). The rSK relation, rSFMS, and rMGMS all show significant galaxy-to-galaxy variation in both shape and normalization, indicating that none of these relations is universal between galaxies. The rSFMS shows the largest galaxy-to-galaxy variation and the rMGMS the least. By defining an ‘offset’ from the average relations, we compute a ΔrSK, ΔrSFMS, ΔrMGMS for each galaxy, to investigate correlations with global properties. We find the following correlations with at least 2σ significance: The rSK is lower (i.e. lower star formation efficiency) in galaxies with higher M⋆, larger Sersic index, and lower specific SFR (sSFR); the rSFMS is lower (i.e. lower sSFR) in galaxies with higher M⋆ and larger Sersic index; and the rMGMS is lower (i.e. lower gas fraction) in galaxies with lower sSFR. In the ensemble of all 15 000 data points, the rSK relation and rMGMS show equally tight scatters and strong correlation coefficients, compared with a larger scatter and weaker correlation in the rSFMS. Moreover, whilst there is no correlation between ΔrSK and ΔrMGMS in the sample, the offset of a galaxy’s rSFMS does correlate with both of the other two offsets. Our results therefore indicate that the rSK and rMGMS are independent relations, whereas the rSFMS is a result of their combination.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".