Characteristics of magnetic solar-like cycles in a 3D MHD simulation of solar convection
Bibliographic record
Abstract
We analyse the statistical properties of the stable magnetic cycle unfolding in an extended 3D magnetohydrodynamic simulation of solar convection produced with the EULAG-MHD code. The millennium simulation spans over 1650 years, in the course of which forty polarity reversals take place on a regular ~40 yr cadence, remaining well-synchronized across solar hemispheres. In order to characterize this cycle and facilitate its comparison with measures typically used to represent solar activity, we build two proxies for the magnetic field in the simulation mimicking the solar toroidal field and the polar radial field. Several quantities that characterize the cycle are measured (period, amplitudes, etc.) and correlations between them are computed. These are then compared with their observational analogs. From the typical Gnevyshev-Ohl pattern, to hints of Gleissberg modulation, the simulated cycles share many of the characteristics of their observational analogs even though the simulation lacks poloidal field regeneration through active region decay, a mechanism nowadays often considered an essential component of the solar dynamo. Some significant discrepancies are also identified, most notably the in-phase variation of the simulated poloidal and toroidal large-scale magnetic components, and the low degree of hemispheric coupling at the level of hemispheric cycle amplitudes. Possible causes underlying these discrepancies are discussed.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".