Mid-infrared interferometry of massive young stellar objects
Bibliographic record
Abstract
The formation scenario for massive stars is still under discussion. To further constrain current theories, it is vital to spatially resolve the structures from which material accretes onto massive young stellar objects (MYSOs). Due to the small angular extent of MYSOs, one needs to overcome the limitations of conventional thermal infrared imaging, regarding spatial resolution, in order to get observational access to the inner structure of these objects. We employed mid-infrared interferometry, using the MIDI instrument on the ESO/VLTI, to investigate the Kleinmann-Wright Object, a massive young stellar object previously identified as a Herbig Be star precursor. Dispersed visibility curves in the N-band (8–13 μm) have been obtained at 5 interferometric baselines. We show that the mid-infrared emission region is resolved. A qualitative analysis of the data indicates a non-rotationally symmetric structure, e.g. the projection of an inclined disk. We employed extensive radiative transfer simulations based on spectral energy distribution fitting. Since SED-only fitting usually yields degenerate results, we first employed a statistical analysis of the parameters provided by the radiative transfer models. In addition, we compared the ten best-fitting self-consistent models to the interferometric observations. Our analysis of the Kleinmann-Wright Object suggests the existence of a circumstellar disk of 0.1 M⊙ at an intermediate inclination of 76°, while an additional dusty envelope is not necessary for fitting the data. Furthermore, we demonstrate that the combination of IR interferometry with radiative transfer simulations has the potential to resolve ambiguities arising from the analysis of spectral energy distributions alone.
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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) | 0.000 | 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 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".