Revealing hidden variability in PWNe with spectral index maps
Bibliographic record
Abstract
ABSTRACT Pulsar wind nebulae (PWNe) are the synchrotron bubbles inflated by the rotational energy of a neutron star. Observing variability within them has previously been limited to cases of significant brightening, or the few instances where transient features are interpreted in terms of intrinsic motion or associated with variability from the pulsar. Jet and torus morphology are also only visible in cases of differing brightness with respect to the surrounding nebula and favourable alignment with our line of sight. Spectral map analysis involves binning observations with an adaptive algorithm to meet a signal limit and colouring the results based on the desired model parameter fits. Minute changes in spectral index become therefore apparent even in cases where brightness images alone do not suggest any underlying changes. We present a Chandra X-ray study of the PWNe in G21.5–0.9, Kes 75, G54.1+0.3, G11.2–0.3, and 3C 58, using archival observations accumulated over the ∼20-yr lifetime of the mission. With the spectral map analysis technique, we discover evidence for previously unknown variability opening a new window into viewing PWNe.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".