Reverberation of pulsar wind nebulae – II. Anatomy of the ‘thin-shell’ evolution
Bibliographic record
Abstract
ABSTRACT During its early evolution, a pulsar wind nebula (PWN) sweeps the inner part of the supernova ejecta and forms a thin massive shell. Later on, when the shell has been reached by the reverse shock of the supernova remnant, the evolution becomes more complex, in most cases reverting the expansion into a compression: this later phase is called ‘reverberation’. Computations done so far to understand this phase have been mostly performed in the thin-shell approximation, where the evolution of the PWN radius is assimilated to that of the swept-up shell under the effect of both the inner pressure from the PWN, and the outer pressure from the supernova remnant. Despite the thin-shell approach seems rather justifiable, its implementations have so far been inaccurate, and its correctness, never tested. The outer pressure was naively assumed to be scaled according to the Sedov solution (or a constant fraction of it) along the entire evolution. The thin-shell assumption itself fails along the process, being the shell no longer thin in comparison with the size of the PWN. Here, through a combination of numerical models, dimensional arguments, and analytical approximations, we present a detailed analysis of the interaction of the PWN with the supernova remnant. We provide a new analytical approximation of the outer pressure, beyond the Sedov solution, and a revised ‘thin-shell’ able to reproduce results from numerical simulations. Finally, we compute the efficiency by which the PWN is compressed during reverberation over a wide population of sources.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".