Investigating the link between inner gravitational potential and star-formation quenching in CALIFA galaxies
Bibliographic record
Abstract
It has been suggested that gravitational potential can have a significant role in suppressing star formation in nearby galaxies. To establish observational constraints on this scenario, we investigated the connection between the dynamics – taking the circular velocity curves (CVCs) as a proxy for the inner gravitational potential – and star formation quenching in 215 non-active galaxies across the Hubble sequence from the Calar Alto Legacy Integral Field Area (CALIFA) survey. Our results show that galaxies with similar CVCs tend to have a certain star-formation quenching pattern. To explore these findings in more details, we constructed kiloparsec(kpc)-resolved relations of the equivalent width of the Hα (WHα) versus the amplitude (Vc) and shape (β = dlnVc/dlnR) of the circular velocity at given radii. We find that the WHα − Vc is a declining relationship, where the retired regions of the galaxies (the ones with WHα values of below 3 Å) tend to have higher Vc. Concurrently, WHα − β is a bimodal relationship, which is characterised by two peaks: concentration of the star forming regions at a positive β (rising CVC) and a second concentration of the retired regions with a negative β (declining CVC). Our results show that both the amplitude of the CVC – driven by the mass of the galaxies – and its shape – which reflects the internal structure of the galaxies – play an important role in the quenching history of a galaxy.
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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".