Self‐organized Critical Model of Energy Release in an Idealized Coronal Loop
Bibliographic record
Abstract
We present and discuss a new avalanche model for solar flares, based on an idealized representation of a coronal loop as a bundle of magnetic flux strands wrapping around one another. The model is based on a two-dimensional cellular automaton with anisotropic connectivity, where linear ensembles of interconnected nodes define the individual strands collectively making up the coronal loop. The system is driven by random deformation of the strands, and a form of reconnection is assumed to take place when the angle subtended by two strands crossing at the same lattice site exceed some preset threshold. Driven in this manner, the cellular automaton produces avalanches of reconnection events characterized by scale-free size distributions that compare favorably with the corresponding size distribution of solar flares, as inferred observationally. Although lattice-based and highly idealized, the model satisfies the constraints Δ · B = 0 by design and is defined in such a way as to be readily mapped back onto coronal loops with set physical dimensions. Carrying this exercise for a generic coronal loop of length 10 10 cm and diameter 10 8 cm yields flare energies ranging from 10 23 to 10 29 erg, for an instability threshold angle of 11° between contiguous magnetic flux strands. These figures square well with both observational determinations and theoretical estimates.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".