On the origin of red giant depletion through low-velocity collisions
Bibliographic record
Abstract
We investigate a means of explaining the apparent paucity of red giant stars within post-core-collapse globular clusters. We propose that collisions between the red giants and binary systems can lead to the destruction of some proportion of the red giant population, by either knocking out the core of the red giant or by forming a common envelope system which will lead to the dissipation of the red giant envelope. Treating the red giant as two point masses, one for the core and another for the envelope (with an appropriate force law to take account of the distribution of mass), and the components of the binary system also treated as point masses, we utilize a four-body code to calculate the time-scales on which the collisions will occur. We then perform a series of smooth particle hydrodynamics runs to examine the details of mass transfer within the system. In addition, we show that collisions between single stars and red giants lead to the formation of a common envelope system which will destroy the red giant star. We find that low-velocity collision between binary systems and red giants can lead to the destruction of up to 13 per cent of the red giant population. This could help to explain the colour gradients observed in PCC globular clusters. We also find that there is the possibility that binary systems formed through both sorts of collision could eventually come into contact perhaps producing a population of cataclysmic variables.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".