RIGEL: Feedback-regulated cloud-scale star formation efficiency in a simulated dwarf galaxy merger
Bibliographic record
Abstract
Major mergers of galaxies are likely to trigger bursty star formation activities. Usually, the accumulation of dense gas and the boost of star formation efficiency (SFE) are considered to be the two main drivers of starbursts. However, it remains unclear how each process operates on the scale of individual star-forming clouds. Here, we present a high-resolution (2 M ⊙ ) radiation-hydrodynamic simulation of a gas-rich dwarf galaxy merger using the Realistic ISM modeling in Galaxy Evolution and Lifecycles (RIGEL) model to investigate how mergers affect the properties of the structure of dense star-forming gas and the cloud-scale SFE. With the unprecedented mass and temporal resolution of the simulations, we tracked the evolution of sub-virial dense clouds in the simulation by mapping them across successive snapshots spanning 200 Myr taken at intervals of 0.2 Myr. We find that the merger triggers a 130 fold increase in the star formation rate (SFR) and shortens the galaxy-wide gas-depletion time by two orders of magnitude compared to those in two matched isolated galaxies. However, the depletion time of individual clouds and their lifetime distribution remained unchanged over the simulation period. The cloud life cycles and cloud-scale SFE are determined by local stellar feedback rather than such environmental factors as tidal fields regardless of the merger process, and the integrated SFE ( ϵ int ) of clouds in complex environments remains well-described by an ϵ int –Σ tot relation found in idealized isolated-cloud experiments. During the peak of the starburst, the median cloud-scale integrated SFE was lower by only 0.17–0.33 dex compared to the value when the two galaxies were not interacting. The merger boosts the SFR primarily through the accumulation and compression of dense gas fueling star formation. Strong tidal torques assemble ≳ 10 5 M ⊙ clouds, which seed massive stellar clusters. The average separation between star-forming clouds decreases during the merger, which in turn decreases the cloud–cluster spatial de-correlation from ≳1 kpc to ∼0.1 kpc depicted in tuning fork diagrams – a testable prediction for future observations of interacting low-mass galaxies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".