SPIN EVOLUTION OF ACCRETING YOUNG STARS. II. EFFECT OF ACCRETION-POWERED STELLAR WINDS
Bibliographic record
Abstract
We present a model for the rotational evolution of a young, solar-mass star interacting magnetically with an accretion disk. As in a previous paper (Paper I), the model includes changes in the star's mass and radius as it descends the Hayashi track, a decreasing accretion rate, and a prescription for the angular momentum transfer between the star and disk. Paper I concluded that, for the relatively strong magnetic coupling expected in real systems, additional processes are necessary to explain the existence of slowly rotating pre-main-sequence stars. In the present paper, we extend the stellar spin model to include the effect of a spin-down torque that arises from an accretion-powered stellar wind (APSW). For a range of magnetic field strengths, accretion rates, initial spin rates, and mass outflow rates, the modeled stars exhibit rotation periods within the range of 1–10 days in the age range of 1–3 Myr. This range coincides with the bulk of the observed rotation periods, with the slow rotators corresponding to stars with the lowest accretion rates, strongest magnetic fields, and/or highest stellar wind mass outflow rates. We also make a direct, quantitative comparison between the APSW scenario and the two types of disk-locking models (namely, the X-wind and Ghosh & Lamb type models) and identify some remaining theoretical issues for understanding young star spins.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".