FORCASTing the Spectroscopic Dust Properties of the WC+O Binary WR 137 with SOFIA
Bibliographic record
Abstract
Abstract WR 137 (HD 192641) is a binary system consisting of a carbon-rich Wolf–Rayet (W-R) star and an Oe companion star in a 13 yr orbit. Near periastron, the winds of the two stars collide and form carbonaceous dust. We obtained three mid-infrared grism spectra of the system with SOFIA and FORCAST during the last year of SOFIA’s operations in 2021 July, 2021 February, and 2022 May (Cycle 9). Within these spectra, we have identified several wind lines from He i, He ii, C iii, and C iv that are emitted from the W-R wind as well as a weak emission feature around 6.3–6.4 μm that may have shifted its peak flux from 6.29 to 6.41 μm through this time period. The weak feature grew as the continuum dust emission grew while the W-R emission appeared to decline due to lower contrast with the continuum. Furthermore, we observe that the peak of the feature shifts to redder wavelengths during the observations. We compare this feature to the unidentified infrared feature and other emission lines identified in dusty carbon-rich W-R (WC) binaries. For WR 137, we speculate that mixing of the winds in the system with the Oe star’s disk is important for starting the dust formation and that it is less important as dust formation continues. Previous infrared photometry shows “minieruptions” of dust production, which could then be explained with variations of the Oe star disk.
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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 | 0.000 | 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".