Transferring Data from a Voronoi Mesh to an Adaptive Cartesian Grid in Pursuit of Self-consistent Top-down Star Formation
Bibliographic record
Abstract
Abstract Unstructured Voronoi mesh simulations offer many advantages for simulating self-gravitating gas dynamics on galactic scales. Adaptive mesh refinement (AMR) can be a powerful tool for simulating the details of star cluster formation and gas dispersal by stellar feedback. Zooming in from galactic to local scales using the star cluster formation simulation package Torch requires transferring simulation data from one scale to the other. Therefore, we introduce VorAMR , a novel computational tool that interpolates data from an unstructured Voronoi mesh to an AMR Cartesian grid. VorAMR is integrated into the Torch package, which integrates the FLASH AMR magnetohydrodynamics code into the Astrophysical Multipurpose Software Environment. VorAMR interpolates data from an AREPO simulation to a FLASH AMR grid using a nearest-neighbor particle scheme, which can then be evolved within the Torch package, representing the first ever transfer of data from a Voronoi mesh to an AMR Cartesian grid. Interpolation from one numerical representation to another results in an error of a few percent in global mass and energy conservation, which could be reduced with higher-order interpolation of the Voronoi cells. We show that the postinterpolation Torch simulation evolves without numerical abnormalities. A preliminary Torch simulation is evolved for 3.22 Myr and compared to the original AREPO simulation over the same time period. We observe similarly distributed star cluster formation between the two simulations. More compact clusters are produced in the Torch simulation as well as 2.3 times as much stellar material as in AREPO , likely due to the differences in resolution.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".