A dust twin of Cas A: cool dust and 21 μm silicate dust feature in the supernova remnant G54.1+0.3
Bibliographic record
Abstract
We present infrared (IR) and submillimetre observations of the Crab-like supernova remnant (SNR) G54.1+0.3 including 350|$\mu$|m (SHARC-II), 870|$\mu$|m (LABOCA), 70, 100, 160, 250, 350, and 500 |$\mu$|m (Herschel), and 3–40 |$\mu$|m (Spitzer). We detect dust features at 9, 11, and 21 |$\mu$|m and a long-wavelength continuum dust component. The 21 |$\mu$|m dust coincides with [Ar ii] ejecta emission, and the feature is remarkably similar to that in Cas A. The IRAC 8|$\mu$|m image including Ar ejecta is distributed in a shell-like morphology which is coincident with dust features, suggesting that dust has formed in the ejecta. We create a cold dust map that shows excess emission in the northwestern shell. We fit the spectral energy distribution of the SNR using the continuous distributions of ellipsoidal grain model of pre-solar grain SiO2 that reproduces the 21 and 9 |$\mu$|m dust features and discuss grains of silicon carbide and polycyclic aromatic hydrocarbon that may be responsible for the 10–13 |$\mu$|m dust features. To reproduce the long-wavelength continuum, we explore models consisting of different grains including Mg2SiO4, MgSiO3, Al2O3, FeS, carbon, and Fe3O4. We tested a model with a temperature-dependent silicate absorption coefficient. We detect cold dust (27–44 K) in the remnant, making this the fourth such SNR with freshly formed dust. The total dust mass in the SNR ranges from |$0.08\text{ to }0.9\, {\rm M}_{\odot }$| depending on the grain composition, which is comparable to predicted masses from theoretical models. Our estimated dust masses are consistent with the idea that SNe are a significant source of dust in the early Universe.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".