ASTRO-H White Paper - Older Supernova Remnants and Pulsar Wind Nebulae
Bibliographic record
Abstract
Most supernova remnants (SNRs) are old, in the sense that their structure has been profoundly modified by their interaction with the surrounding interstellar medium (ISM). Old SNRs are very heterogenous in terms of their appearance, reflecting differences in their evolutionary state, the environments in which SNe explode and in the explosion products. Some old SNRs are seen primarily as a result of a strong shock wave interacting with the ISM. Others, the so-called mixed-morphology SNRs, show central concentrations of emission, which may still show evidence of emission from the ejecta. Yet others, the pulsar wind nebulae (PWNe), are seen primarily as a result of emission powered by a pulsar; these SNRs often lack the detectable thermal emission from the primary shock. The underlying goal in all studies of old SNRs is to understand these differences, in terms of the SNe that created them, the nature of the ISM into which they are expanding, and the fundamental physical processes that govern their evolution. Here we identify three areas of study where ASTRO-H can make important contributions. These are constraining abundances and physical processes in mature limb-brightened SNRs, understanding the puzzling nature of mixed-morphology SNRs, and exploring the nature of PWNe. The Soft X-ray Spectrometer (SXS) on-board ASTRO-H will, as a result of its high spectral resolution, be the primary tool for addressing problems associated with old SNRs, supported by hard X-ray observations with the Hard X-ray Imager (HXI) to obtain broad band X-ray coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.038 |
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".