Dust and Molecule Formation and Processing in Supernovae and their Remnants
Bibliographic record
Abstract
Abstract Supernovae (SNe) produce, fragment and destroy dust, molecules and nucleosynthetic elements, and reshape and modify the ISM. I will review recent infrared observations of supernova remnants (SNRs) and SNe which show that SNe are important sites of dust and molecule formation and are major dust creators in the Universe. Detection of carbon monoxide (CO) fundamental band from the young SNR Cas A indicates that astrochemical processes in SNRs interacting with molecular clouds provide astrophysical laboratories to study evolution of the ISM returning material from dense clouds into the more diffuse medium and galactic halo. Two dozen SNRs are known to be interacting with molecular clouds using H2 and millimeter observations. Recent Spitzer, Herschel and SOFIA observations along with ground-based observations have greatly advanced our understanding shock processing and astrochemistry of dust, H2, high J CO, and other neutral and ionized molecules and polycyclic aromatic hydrocarbon (PAH). Ionized molecules and warm layer of molecules that are excited by UV radiation, X-rays, or cosmic rays will be described. Finally I will discuss how astrochemical processes of dust and molecules in SNRs impact the large scale structures in the ISM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".