The collision and merger products of stars do not look alike: A magnetohydrodynamics comparison
Bibliographic record
Abstract
A significant fraction of stars experience close interactions, including collisions resulting from gravitational encounters and mergers within close binary systems. These processes can produce more massive stars that may give rise to relatively rare objects such as blue stragglers. Distinguishing the outcomes of collisions and mergers is challenging yet essential for interpreting observations. This study utilizes the magnetohydrodynamics code AREPO to simulate collisions and mergers of $5$ to $10 \,\mathrm{M}_{\odot}$ main-sequence stars, systematically comparing the properties of the resulting products. Both collisions and mergers yield more massive, strongly magnetized, rapidly and differentially rotating stars with cores enriched in hydrogen, but notable quantitative differences emerge. Merger products exhibit core hydrogen fractions up to $10\%$ higher than those of collision products. In both scenarios, turbulent mixing amplifies magnetic field energies by $9$ to $12$ orders of magnitude. However, magnetic fields in small-impact-parameter collision products display small-scale reversals that may dissipate over time, whereas merger products and large-impact-parameter collision products develop large-scale ordered, potentially long-lived magnetic fields. Additionally, only merger products display magnetically driven, bipolar outflows with radial velocities exceeding $300$ to $400 \,\mathrm{km}\,\mathrm{s}^{-1}$. These distinctions may result in different long-term evolutionary outcomes, which warrant further investigation in future studies.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".