The impact of merging on the origin of kinematically misaligned and counter-rotating galaxies in MaNGA
Bibliographic record
Abstract
ABSTRACT Galaxy mergers and interactions are expected to play a significant role leading to offsets between gas and stellar motions in galaxies. Herein, we cross-match galaxies in Mapping Nearby Galaxies at Apache Point Observatory MPL-8 with the Dark Energy Spectroscopic Instrument Legacy Surveys and identify 311 merging galaxies that have reliable measurements of ΔPA, the difference between the stellar and gas kinematic position angles, to investigate the impacts of merging on gas–stellar rotation misalignments. We find that the merging fractions of misaligned galaxies (30$^\circ \, \leqslant \, \Delta$PA < 150○) are higher than those of co-rotators (ΔPA < 30○) in both quiescent and star-forming galaxies. This result suggests that merging is one process to produce kinematic misalignments. The merging fraction of counter-rotators (ΔPA ≥ 150○) is lower than that of misaligned galaxies in both quiescent and star-forming galaxies, while in the latter it is likely even lower than that of co-rotators. The orbital angular momentum transfer to the spins of stars and gas during merging and the tidal feature disappearance can lead to small merging fractions in counter-rotators. Numerous new stars that inherit angular momentum from gas after merging can further lower the merging fraction of star-forming counter-rotators.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".