On the effects of photoionization feedback on second-generation star formation in globular clusters of different masses
Bibliographic record
Abstract
ABSTRACT We simulate the formation of second-generation (SG) stars in young clusters with masses of 105 and $10^6\, \mathrm{M}_{\odot }$ within $30\!-\!100\, \mathrm{Myr}$ after the formation of clusters. We assume the clusters move through a uniform interstellar medium with gas densities of 10−24 and $10^{-23}\, \mathrm{g\, cm}^{-3}$ and consider the stellar winds from asymptotic giant branch (AGB) stars, gas accretion on to the cluster, ram pressure, star formation, and photoionization feedback of our stellar systems including binary stars. We find that SG stars can be formed only within the $10^6\, \mathrm{M}_{\odot }$ cluster in the high-density simulation, where the cluster can accrete sufficient pristine gas from their surrounding medium, leading to efficient cooling required for the ignition of SG formation and sufficient dilution of the AGB ejecta. Hence, our results indicate that a denser environment is another requirement for the AGB scenario to explain the presence of multiple populations in globular clusters. On the other hand, the ionizing feedback becomes effective in heating the gas in our low-density simulations. As a result, the clusters cannot accumulate a considerable amount of pristine gas at their centre. The gas mass within the clusters in these simulations is similar to that in young massive clusters (YMCs). Hence, our studies can provide a possible reason for the lack of gas, star formation, and SG stars in YMCs. Our results indicate that the ionizing stellar feedback is not a severe problem for SG formation; rather, it can help the AGB scenario to account for some observables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".