Fuelling the nuclear ring of NGC 1097
Bibliographic record
Abstract
ABSTRACT Galactic bars can drive cold gas inflows towards the centres of galaxies. The gas transport happens primarily through the so-called bar dust lanes, which connect the galactic disc at kpc scales to the nuclear rings at hundreds of pc scales much like two gigantic galactic rivers. Once in the ring, the gas can fuel star formation activity, galactic outflows, and central supermassive black holes. Measuring the mass inflow rates is therefore important to understanding the mass/energy budget and evolution of galactic nuclei. In this work, we use CO datacubes from the PHANGS-ALMA survey and a simple geometrical method to measure the bar-driven mass inflow rate on to the nuclear ring of the barred galaxy NGC 1097. The method assumes that the gas velocity in the bar lanes is parallel to the lanes in the frame co-rotating with the bar, and allows one to derive the inflow rates from sufficiently sensitive and resolved position–position–velocity diagrams if the bar pattern speed and galaxy orientations are known. We find an inflow rate of $\dot{M}=(3.0 \pm 2.1)\, \rm M_\odot \, yr^{-1}$ averaged over a time span of 40 Myr, which varies by a factor of a few over time-scales of ∼10 Myr. Most of the inflow appears to be consumed by star formation in the ring, which is currently occurring at a star formation rate (SFR) of $\simeq\!1.8\!-\!2 \, \rm M_\odot \, yr^{-1}$, suggesting that the inflow is causally controlling the SFR in the ring as a function of time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".