Fragmentation And Evolution Of Molecular Clouds. III. The Effect Of Dust And Gas Energetics
Bibliographic record
Abstract
Dust and gas energetics are incorporated into a cluster-scale simulation of star formation in order to study the effect of heating and cooling on the star formation process. We build on our previous work by calculating separately the dust and gas temperatures. The dust temperature is set by radiative equilibrium between heating by embedded stars and radiation from dust. The gas temperature is determined using an energy-rate balance algorithm which includes molecular cooling, dust-gas collisional energy transfer, and cosmic-ray ionization. The fragmentation proceeds roughly similarly to simulations in which the gas temperature is set to the dust temperature, but there are differences. The structure of regions around sink particles has properties similar to those of Class 0 objects, but the infall speeds and mass accretion rates are, on average, higher than those seen for regions forming only low-mass stars. The gas and dust temperature have complex distributions not well modeled by approximations that ignore the detailed thermal physics. There is no simple relationship between density and kinetic temperature. In particular, high-density regions have a large range of temperatures, determined by their location relative to heating sources. The total luminosity underestimates the star formation rate at these early stages, before ionizing sources are included, by an order of magnitude. As predicted in our previous work, a larger number of intermediate-mass objects form when improved thermal physics is included, but the resulting initial mass function (IMF) still has too few low-mass stars. However, if we consider recent evidence on core-to-star efficiencies, the match to the IMF is improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".