Oort Cloud Formation and Evolution in Star Clusters
Bibliographic record
Abstract
Abstract It is unknown whether an Oort cloud reaches its maximum mass within its star’s birth cluster or millions of years later. Complicating the Oort cloud evolution process is the fact that comets can be stripped from orbit due to perturbations from passing stars. We explore how a star’s cluster escape time (t esc) and the time its Oort cloud reaches maximum mass (t max ) affect the Oort cloud’s ability to survive via N-body simulations. In a 14 M ⊙ pc–3 cluster, we identify 50 stars of 1 M ⊙ with a range of t esc to host Oort clouds, each with 1000 comets at t max . For each host, we consider Oort clouds that reach maximum mass 0, 50, and 250 Myr after the cluster’s formation. Each Oort cloud’s evolution is simulated in the cluster from t max to t esc. Only a fraction of comets tend to remain in orbit, with this amount depending on t max and t esc. We observe that 12%, 22%, and 32% of Oort clouds with a t max of 0, 50, and 250 Myr retain >50% of their comets at t esc, respectively. We find that the fraction of comets stripped has the relationship f = m log 10 t esc − t max Myr , where m = 0.32 ± 0.04, indicating that the longer the Oort cloud remains in the cluster, the more comets are stripped, with this fraction increasing logarithmically at approximately the same rate for each t max .
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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 | 0.001 | 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 | 0.000 |
| 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".