General-relativistic lattice-Boltzmann method for radiation transport
Bibliographic record
Abstract
ABSTRACT We present the first extension of the special-relativistic lattice-Boltzmann method for radiative transport to solve the radiative-transfer equation in curved space–times. The novel approach is based on the streaming of carefully selected photons along null geodesics and interpolating their final positions, velocities, and frequency shifts to all photons in a given velocity stencil. Furthermore, by transforming between the laboratory frame, the Eulerian frame, and the fluid frame, we are able to perform the collision step in the fluid frame, thus retaining the collision operator of the special-relativistic case with only minor modifications. As a result, with the new method we can model the evolution of the frequency-independent (grey) radiation field as it interacts with a background fluid via absorption, emission, and scattering in a curved background space–time. Finally, by introducing a refined adaptive stencil, which is suitably distorted in the direction of propagation of the photon bundle, we can reduce the computational costs of the method while improving its performance in the optically thin regime. A number of standard and novel tests are presented to validate the approach and exhibit its robustness and accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".