The Dynamics, Destruction, and Survival of Supernova-formed Dust Grains
Bibliographic record
Abstract
Abstract Observations have demonstrated that supernovae efficiently produce dust. This is consistent with the hypothesis that supernovae and asymptotic giant branch stars are the primary producers of dust in the universe. However, there has been a longstanding question of how much of the dust detected in the interiors of young supernova remnants can escape into the interstellar medium. We present new hydrodynamical calculations of the evolution of dust grains that were formed in dense ejecta clumps within a Cas A–like remnant. We follow the dynamics of the grains as they decouple from the gas after their clump is hit by the reverse shock. They are subsequently subject to destruction by thermal and kinetic sputtering as they traverse the remnant. Grains that are large enough (∼0.25 μm for silicates and ∼0.1 μm for carbonaceous grains) escape into the interstellar medium while smaller grains get trapped and destroyed. However, grains that reach the interstellar medium still have high velocities, and are subject to further destruction as they are slowed down. We find that for initial grain size distributions that include large (∼0.25–0.5 μm) grains, 10%–20% of silicate grains can survive, while 30–50% of carbonaceous grains survive even when the initial size distribution cuts off at smaller (0.25 μm) sizes. For a 19 M ⊙ star similar to the progenitor of Cas A, up to 0.1 M ⊙ of dust can survive if the dust grains formed are large. Thus we show that supernovae under the right conditions can be significant sources of interstellar dust.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".