Evolution of dust in the Orion Bar with Herschel: I. Radiative transfer modelling
Bibliographic record
Abstract
Interstellar dust is a key element in our understanding of the interstellar medium and star formation. The manner in which dust populations evolve with the excitation and the physical conditions is a first step in the comprehension of the evolution of inter- stellar dust. Within the framework of the Evolution of interstellar dust Herschel key program, we have acquired PACS and SPIRE spec- trophotometric observations of various photodissociation regions, to characterise this evolution. The aim of this paper is to trace the evolution of dust grains in the Orion Bar photodissociation region. We use Herschel/PACS (70 and 160 mic) and SPIRE (250, 350 and 500 mic) together with Spitzer/IRAC observations to map the spatial distribution of the dust populations across the Bar. Brightness profiles are modelled using the DustEM model coupled with a radiative transfer code. Thanks to Herschel, we are able to probe finely the dust emission of the densest parts of the Orion Bar with a resolution from 5.6" to 35.1". These new observations allow us to infer the temperature of the biggest grains at different positions in the Bar, which reveals a gradient from \sim 80 K to 40 K coupled with an increase of the spectral emissivity index from the ionization front to the densest regions. Combining Spitzer/IRAC observations, which are sensitive to the dust emission from the surface, with Herschel maps, we have been able to measure the Orion Bar emission from 3.6 to 500 mic. We find a stratification in the different dust components which can be re- produced quantitatively by a simple radiative transfer model without dust evolution. However including dust evolution is needed to explain the brightness in each band. PAH abundance variations, or a combination of PAH abundance variations with an emissivity enhancement of the biggest grains due to coagulation give good results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".