Applying the Tremaine–Weinberg Method to Nearby Galaxies: Stellar-mass-based Pattern Speeds and Comparisons with ISM Kinematics
Bibliographic record
Abstract
Abstract We apply the Tremaine–Weinberg method to 19 nearby galaxies using stellar mass surface densities and velocities derived from the PHANGS-MUSE survey, to calculate (primarily bar) pattern speeds (Ω P ). After quality checks, we find that around half (10) of these stellar-mass-based measurements are reliable. For those galaxies, we find good agreement between our results and previously published pattern speeds, and we use rotation curves to calculate major resonance locations (corotation radii and Lindblad resonances). We also compare these stellar-mass-derived pattern speeds with H α (from MUSE) and CO( J = 2 − 1) emission from the PHANGS-ALMA survey. We find that in the case of these clumpy interstellar medium (ISM) tracers, this method erroneously gives a signal that is simply the angular frequency at a representative radius set by the distribution of these clumps (Ω clump ), and that this Ω clump is significantly different from Ω P (∼20% in the case of H α , and ∼50% in the case of CO). Thus, we conclude that it is inadvisable to use “pattern speeds” derived from ISM kinematics. Finally, we compare our derived pattern speeds and corotation radii, along with bar properties, to the global parameters of these galaxies. Consistent with previous studies, we find that galaxies with a later Hubble type have a larger ratio of corotation radius to bar length, more molecular-gas-rich galaxies have higher Ω P , and more bulge-dominated galaxies have lower Ω P . Unlike earlier works, however, there are no clear trends between the bar strength and Ω P , nor between the total stellar mass surface density and the pattern speed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".