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 submillimeter observations of the Crab-like supernova remnant (SNR) G54.1+0.3 including 350μm (SHARC-II), 870μm (LABOCA), 70, 100, 160, 250, 350, 500μm (Herschel) and 3-40μm (Spitzer). We detect dust features at 9, 11 and 21μm and a long wavelength continuum dust component. The 21μm dust coincides with [Ar II] ejecta emission, and the feature is remarkably similar to that in Cas A. The IRAC 8μm 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 (CDE) grain model of pre-solar grain SiO2 that reproduces the 21 and 9μm dust features and discuss grains of SiC and PAH that may be responsible for the 10-13μm 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 − 0.9 M⊙ 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".