Gas flows in galaxy mergers
Bibliographic record
Abstract
In major galaxy mergers, the orbits of stars are violently perturbed, and gas is torqued to the centre, diluting the gas metallicity and igniting a starburst. In this paper, we study the gas dynamics in and around merging galaxies using a series of cosmological magnetohydrodynamical zoom-in simulations. We find that the gas bridge connecting the merging galaxies pre-coalescence is dominated by turbulent pressure, with turbulent Mach numbers peaking at values of 1.6-3.3. This implies that bridges are dominated by supersonic turbulence, and are thus ideal candidates for studying the impact of extreme environments on star formation. We also find that gas accreted from the circumgalactic medium (CGM) during the merger significantly contributes (27-51 percent) to the star formation rate (SFR) at the time of coalescence and drives the subsequent reignition of star formation in the merger remnant. Indeed, 19-53 percent of the SFR at z = 0 originates from gas belonging to the CGM prior the merger. Finally, we investigate the origin of the metallicity-diluted gas at the centre of merging galaxies. We show that this gas is rapidly accreted on to the Galactic Centre with a time-scale much shorter than that of normal star-forming galaxies. This explains why coalescing galaxies are not well-captured by the fundamental metallicity relation.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".