Magnetic Reconnection in Astrophysical Environments
Bibliographic record
Abstract
Abstract Magnetic reconnection is a process that changes magnetic field topology in highly conducting fluids. Traditionally, magnetic reconnection was associated mostly with solar flares. In reality, the process must be ubiquitous as astrophysical fluids are magnetized and motions of fluid elements necessarily entail crossing of magnetic frozen in field lines and magnetic reconnection. We consider magnetic re-connection in realistic 3D geometry in the presence of turbulence. This turbulence in most astrophysical settings is of pre-existing nature, but it also can be induced by magnetic reconnection itself. In this situation turbulent magnetic field wander-ing opens up reconnection outflow regions, making reconnection fast. We discuss Lazarian & Vishniac (1999) model of turbulent reconnection, its numerical and ob-servational testings, as well as its connection to the modern understanding of the Lagrangian properties of turbulent fluids. We show that the predicted dependences of the reconnection rates on the level of MHD turbulence make the generally ac-cepted Goldreich & Sridhar (1995) model of turbulence self-consistent. Similarly, we argue that the well-known Alfvén theorem on flux freezing is not valid for the turbulent fluids and therefore magnetic fields diffuse within turbulent volumes. This is an element of magnetic field dynamics that was not accounted by earlier theories. For instance, the theory of star formation that was developing assuming that it is
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".