A wind–shell interaction model for multipolar planetary nebulae
Bibliographic record
Abstract
We explore the formation of multipolar structures in planetary and pre-planetary nebulae from the interaction of a fast post-asymptotic giant branch wind with a highly inhomogeneous and filamentary shell structure assumed to form during the final phase of the high-density wind. The simulations were performed with a new hydrodynamics code integrated in the interactive framework of the astrophysical modelling package shape. In contrast to conventional astrophysical hydrodynamics software, the new code does not require any programming intervention by the user for setting up or controlling the code. Visualization and analysis of the simulation data has been done in shape without external software. The key conclusion from the simulations is that secondary lobes in planetary nebulae, such as Hubble 5 and K3–17, can be formed through the interaction of a fast low-density wind with a complex high-density environment, such as a filamentary circumstellar shell. The more complicated alternative explanation of intermittent collimated outflows that change direction, in many cases, may therefore not be necessary. We consider that the wind–shell interaction scenario is more likely since the bow-shock shape expected from a strongly cooling bow-shock from jets is different from that of the observed bubbles. Furthermore, the time-scales of the wind–wind interaction suggest that the progenitor star was rather massive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".