Evolution of interstellar dust with<i>Herschel</i>. First results in the photodissociation regions of NGC 7023
Bibliographic record
Abstract
Context. In photodissociation regions (PDRs), the physical conditions and the excitation evolve on short spatial scales as a function of depth within the cloud, providing a unique opportunity to study how the dust and gas populations evolve with the excitation and physical conditions. The mapping of the PDRs in NGC 7023 performed during the science demonstration phase of Herschel is part of the “Evolution of interstellar dust” key program. The goal of this project is to build a coherent database on interstellar dust emission from diffuse clouds to the sites of star formation. Aims. We study the far-infrared/submillimeter emission of the PDRs and their fainter surrounding regions. We combine the Herschel and Spitzer maps to derive at each position the full emission spectrum of all dust components, which we compare to dust and radiative transfer models in order to learn about the spatial variations in both the excitation conditions and the dust properties. Methods. We adjust the emission spectra derived from PACS and SPIRE maps using modified black bodies to derive the temperature and the emissivity index β of the dust in thermal equilibrium with the radiation field. We present a first modeling of the NGC 7023-E PDR with standard dust properties and abundances.Results. At the peak positions, a value of β equal to 2 is compatible with the data. The detected spectra and the spatial structures are strongly influenced by radiative transfer effects. We are able to reproduce the spectra at the peak positions deduced from Herschel maps and emitted by dust particles at thermal equilibrium, and also the evolution of the spatial structures observed from the near infrared to the submillimeter. On the other hand, the emission of the stochastically heated smaller particles is overestimated by a factor ~2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".