The resolved star-formation efficiency of early-type galaxies
Bibliographic record
Abstract
ABSTRACT Understanding how and why star formation varies between galaxies is fundamental to our comprehension of galaxy evolution. In particular, the star-formation efficiency (SFE; star-formation rate or SFR per unit cold gas mass) has been shown to vary substantially both across and within galaxies. Early-type galaxies (ETGs) constitute an extreme case, as about a quarter have detectable molecular gas reservoirs but little to no detectable star formation. In this work, we present a spatially resolved view of the SFE in 10 ETGs, combining state-of-the-art Atacama Large Millimeter/submillimeter Array (ALMA) and Multi Unit Spectroscopic Explorer (MUSE) observations. Optical spectroscopic line diagnostics are used to identify the ionized emission regions dominated by star formation, and reject regions where the ionization arises primarily from other sources. We identify very few regions where the ionization is consistent with pure star formation. Using ${\rm H}\alpha$ as our SFR tracer, we find that previous integrated measurements of the star-formation rate based on UV and 22 $\mu$m emission are systematically higher than the SFR measured from ${\rm H}\alpha$. However, for the small number of regions where ionization is primarily associated with star formation, the SFEs are around 0.4 dex higher than those measured in star-forming galaxies at a similar spatial resolution (with depletion times ranging from $10^8$ to $10^{10}$ yr). Whilst the SFE of ETGs is overall low, we find that the SFEs of individual regions within ETGs can be similar to, or higher than, similar sized regions within star-forming galaxies.
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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) | 0.001 | 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".