The Aromatic Infrared Bands around the Wolf–Rayet Binary WR 140 Revealed by JWST
Bibliographic record
Abstract
Abstract We have analyzed the aromatic infrared bands (AIBs) in the 6–11.2 μ m range around the Wolf–Rayet (WR) binary WR 140 ( d = 1.64 kpc) obtained with the James Webb Space Telescope Mid-Infrared Instrument Medium-Resolution Spectrometer (MRS). In WR 140’s circumstellar environment, we have detected AIBs at 6 and 7.7 μ m, which are attributed to C–C stretching modes. These features have been detected in the innermost dust shell (Shell 1; ∼2100 au from WR 140), the subsequent dust shell (Shell 2; ∼5200 au), and “off-shell” regions in the MRS coverage. The 11.2 μ m AIB, which is associated with the C–H out-of-plane bending mode, has been tentatively detected in Shell 2 and the surrounding off-shell positions around Shell 2. We compared the AIB features from WR 140 to spectra of established AIB feature classes A, B, C, and D. The detected features around WR 140 do not agree with these established classes. The peak wavelengths and full width half maxima of the 6 and 7.7 μ m features are, however, consistent with those of R Coronae Borealis stars with hydrogen-poor conditions. We discuss a possible structure of carbonaceous compounds and environments where they form around WR 140. It is proposed that hydrogen-poor carbonaceous compounds initially originate from the carbon-rich WR wind, and the hydrogen-rich stellar wind from the companion O star may provide hydrogen to these carbonaceous compounds.
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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".