STAR FORMATION IN DISK GALAXIES. II. THE EFFECT OF STAR FORMATION AND PHOTOELECTRIC HEATING ON THE FORMATION AND EVOLUTION OF GIANT MOLECULAR CLOUDS
Bibliographic record
Abstract
We investigate the effect of star formation and diffuse photoelectric heating on the properties of giant molecular clouds (GMCs) formed in high-resolution (≲10 pc) global (∼20 kpc) simulations of isolated Milky-Way-type galaxy disks. The clouds are formed through gravitational fragmentation, and structures with densities n H,c >100 cm −3 are identified as GMCs. Between 1000 and 1500 clouds are created in the simulations with masses M >10 5 M ☉ and 180–240 with masses M >10 6 M ☉ in agreement with estimates of the Milky Way's population. We find that the effect of photoelectric heating is to suppress the fragmentation of the interstellar medium, resulting in a filamentary structure in the warm gas surrounding clouds. This environment suppresses the formation of a retrograde rotating cloud population, with 88% of the clouds rotating prograde with respect to the galaxy after 300 Myr. The diffuse heating also reduces the initial star formation rate (SFR), slowing the conversation of gas into stars. We therefore conclude that the interstellar environment plays an important role in the GMC evolution. Our clouds live between 0 and 20 Myr with a high infant mortality ( t ' < 3 Myr) due to cloud mergers and star formation. Other properties, including distributions of mass, size, and surface density, agree well with observations. Collisions between our clouds are common, occurring at a rate of ∼ 1/4 of the orbital period. It is not clear whether such collisions trigger or suppress star formation at our current resolution. Our SFR is a factor of 10 higher than observations in local galaxies. This is likely due to the absence of localized feedback in our models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".