Comparison of magnetic diffusion and reconnection in ideal and resistive relativistic magnetohydrodynamics, ideal magnetodynamics, and resistive force-free electrodynamics
Bibliographic record
Abstract
High-energy astrophysical systems and compact objects are frequently modeled using ideal relativistic magnetohydrodynamic (MHD) or force-free electrodynamic (FFE) simulations, with the underlying assumption that the discretization from the numerical scheme introduces an effective (numerical) magnetic resistivity that adequately resembles an explicit resistivity. However, it is crucial to note that numerical resistivity can fail to replicate essential features of explicit resistivity. In this study, we compare the 1D resistive decay and 2D reconnection properties of four commonly used physical models. We demonstrate that the 1D Ohmic decay of current sheets via numerical dissipation in both ideal MHD and magnetodynamics (MD) is subdiffusive (i.e., sublinear in time), whereas explicit resistive FFE and resistive MHD simulations match the predictions of resistive theory adequately. For low-resolution, reconnecting current sheets in two dimensions, we show that ideal MHD and MD have an analog to the Sweet-Parker regime where the scaling of the reconnection rate depends directly on the resolution. At high resolutions, ideal MHD and MD have an asymptotic reconnection rate similar to resistive MHD. Furthermore, we find that guide field-balanced current sheets in ideal MHD and MD have a qualitative structure similar to that of one in resistive MHD. Similarly, a pressure-balanced current sheet in ideal MHD is found to have a qualitative structure similar to that of one in resistive MHD. For a guide field-balanced sheet, resistive FFE is found to have a nearly identical Sweet-Parker regime compared to resistive MHD and a similar asymptotic reconnection rate for large enough Lundquist numbers, but differs in the timescale for reconnection onset in the asymptotic regime. We discuss the implications of our findings for global simulations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".