Aspects of holographic Langevin diffusion in the presence of anisotropic magnetic field
Bibliographic record
Abstract
We study the holographic Langevin diffusion coefficients of a heavy quark, when traveling through a strongly coupled anisotropic plasma in the presence of magnetic field B . While previous studies have characterized Langevin diffusion coefficients in the magnetic branes model, our analysis uncovers several new features among the five coefficients in this magnetic anisotropic plasma, advancing the understanding of heavy quark dynamics in such environments. In particular, we clarified how motion velocities shape momentum broadening and its directional dependence. It is observed that the transverse Langevin diffusion coefficients depend more on the direction of motion rather than the directions of momentum diffusion at the ultra-fast limit, while an opposite conclusion is found when the moving speed is sufficiently low. For the Longitudinal Langevin diffusion coefficient, we find that motion perpendicular to B affects the Langevin coefficients stronger at any fixed velocity. We should also emphasize that all five Langevin coefficients are becoming larger with increasing velocity. We find that the universal relation κ ∥ > κ ⊥ in the isotropic background, is broken in a different new case that a quark moving paralleled to B . This is one more particular example where the violation of the universal relation occurs for the anisotropic background. Further, we find the critical velocity of the violation will become larger with increasing B .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| 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".