TREVR2: illuminating fast <i>N</i> log2<i>N</i> radiative transfer
Bibliographic record
Abstract
ABSTRACT We present trevr2 (Tree-based REVerse Ray Tracing 2), a fast, general algorithm for computing the radiation field, suitable for both particle and mesh codes. It is designed to self-consistently evolve chemistry for zoomed-in astrophysical simulations, such as cosmological galaxies with both internal sources and prescribed background radiation, rather than large periodic volumes. Light is propagated until absorbed, with no imposed speed limit other than those due to opacity changes (e.g. ionization fronts). trevr2 searches outward from receiving gas in discrete directions set by the healpix algorithm (unlike its slower predecessor trevr), accumulating optical depth and adding the flux due to sources combined into progressively larger tree cells with distance. We demonstrate Nactivelog2N execution time with absorption and many sources. This allows multiband radiative transfer costs comparable to tree-based gravity and hydrodynamics, and the usual speed-up when active particles evolve on individual time-steps. Sources embedded in non-homogeneous absorbing material introduce systematic errors. We introduce transmission averaging instead of absorption averaging which dramatically reduces these systematic effects. We outline other ways to address systematics including an explicit complex source model. We demonstrate the overall performance of the method via a set of astrophysical test problems.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".