The ALMaQUEST Survey – II. What drives central starbursts at z ∼ 0?
Bibliographic record
Abstract
ABSTRACT Starburst galaxies have elevated star formation rates (SFRs) for their stellar mass. In Ellison et al., we used integral field unit maps of SFR surface density (ΣSFR) and stellar mass surface density (Σ⋆) to show that starburst galaxies in the local universe are driven by SFRs that are preferentially boosted in their central regions. Here, we present molecular gas maps obtained with the Atacama Large Millimeter Array (ALMA) observatory for 12 central starburst galaxies at z ∼ 0 drawn from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. The ALMA and MaNGA data are well matched in spatial resolution, such that the ALMA maps of molecular gas surface density ($\Sigma _{\rm H_2}$) can be directly compared with MaNGA maps at kpc-scale resolution. The combination of $\Sigma _{\rm H_2}$, Σ⋆ and ΣSFR at the same resolution allow us to investigate whether central starbursts are driven primarily by enhancements in star formation efficiency (SFE) or by increased gas fractions. By computing offsets from the resolved Kennicutt-Schmidt relation ($\Sigma _{\rm H_2}$ versus ΣSFR) and the molecular gas main sequence (Σ⋆ versus $\Sigma _{\rm H_2}$), we conclude that the primary driver of the central starburst is an elevated SFE. We also show that the enhancement in ΣSFR is accompanied by a dilution in O/H, consistent with a triggering that is induced by metal poor gas inflow. These observational signatures are found in both undisturbed (9/12 galaxies in our sample) and recently merged galaxies, indicating that both interactions and secular mechanisms contribute to central starbursts.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".