Cloud-scale Analyses on Molecular Gas in Simulated and Observed Galaxy Mergers
Bibliographic record
Abstract
Abstract We employ the Feedback In Realistic Environments (FIRE-2) physics model to study how the properties of giant molecular clouds (GMCs) evolve during galaxy mergers. Due to the rarity of mergers in the local Universe, samples of nearby merging galaxies suitable for studies of individual GMCs are limited. Idealized simulations provide us with a new window to study GMC evolution during a merger, and assist in interpreting observations. We conduct a pixel-by-pixel analysis of the simulated molecular gas properties in both undisturbed control galaxies and galaxy mergers. The simulated GMC-pixels follow a similar trend in a diagram of velocity dispersion (σv) versus gas surface density (Σmol) as observed in normal spiral galaxies in the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey. For simulated mergers, we see a significant increase in both the Σmol and σv for GMC-pixels by a factor of 5 – 10, which put these pixels to be above the trend of PHANGS galaxies in the σv vs Σmol diagram. This deviation indicates that GMCs in the simulated merger are more gravitationally unbound and have higher virial parameter (αvir) of 10 – 100, which is much larger than that of simulated control galaxies. Furthermore, we find that the increase in αvir generally happens at the same time as the increase in global star formation rate (SFR), which suggests feedback is playing a role in dispersing the gas. The correspondence between high αvir and SFR also suggests some other physical mechanisms besides self-gravity are helping the GMCs in starburst mergers to collapse and form stars.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".