Far and extreme UV radiation feedback in molecular clouds and its influence on the mass and size of star clusters
Bibliographic record
Abstract
ABSTRACT We study the formation of star clusters in molecular clouds by performing three-dimensional radiation hydrodynamics simulations with far-ultraviolet (FUV; 6 eV≦hν≦13.6 eV) and extreme ultraviolet (EUV; hν≧13.6 eV) radiative feedback. We find that the FUV feedback significantly suppresses the star formation in diffuse clouds with the initial surface densities of $\Sigma _{\rm cl} \lesssim \rm 50~M_{\odot } \,\, pc^{-2}$. In the cases of clouds with $\Sigma _{\rm cl} \sim \rm 100-200~M_{\odot } \,\, pc^{-2}$, the EUV feedback plays a main role and decrease the star formation efficiencies less than 0.3. We show that thermal pressure from photodissociation regions or H ii regions disrupts the clouds and makes the size of the star clusters larger. Consequently, the clouds with the mass $M_{\rm cl} \lesssim 10^{5}~\rm M_{\odot }$ and the surface density $\Sigma _{\rm cl} \lesssim 200~\rm M_{\odot }\,\, pc^{-2}$ remain the star clusters with the stellar densities of $\sim 100~\rm M_{\odot }\,\, pc^{-3}$ that nicely match the observed open clusters in the Milky Way. If the molecular clouds are massive ($M_{\rm cl} \gtrsim 10^{5}~\rm M_{\odot }$) and compact ($\Sigma \gtrsim 400~\rm M_{\odot }\,\, pc^{-2}$), the radiative feedback is not effective and they form massive dense cluster with the stellar densities of $\sim 10^{4}~\rm M_{\odot }\,\, pc^{-3}$ like observed globular clusters or young massive star clusters. Thus, we suggest that the radiative feedback and the initial conditions of molecular clouds are key factors inducing the variety of the observed star clusters.
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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 | 0.000 |
| 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".