Estimating distance, pressure, and dust opacity using submillimeter observations of self-gravitating filaments
Bibliographic record
Abstract
We present a detailed study of the surface brightness profiles of dense filaments in IC 5146 using recent Herschel observations done with SPIRE. We describe the profile through an equilibrium solution of a self-gravitating isothermal cylinder pressure confined by its surrounding medium. In this first analysis we applied a simple modified black body function for the emissivity, neglecting any radiative transfer effects. Overall we found a good agreement of the observed surface brightness profiles with the model. The filaments indicate strong self-gravity with mass line densities M/l > ~0.5(M/l)max where (M/l)max is the maximum possible mass line density. In accordance with the model expectations we found a systematic decrease of the FWHM, a steepening of the density profile, and for filaments heated by the interstellar radiation field a decrease of the luminosity to mass ratio for higher central column density and mass line density. We illustrate and discuss the possibility of estimating the distance, external pressure, and dust opacity. For a cloud distance D ~ 500 pc and a gas temperature of Tcyl = 10 K the model implies an external pressure pext/k ~ 2 × 104 K cm-3 and an effective dust emission coefficient at 250 μm given by δκ0em~0.0588 cm2 g-1 where δ is the dust-to-gas ratio. Given the largest estimate of the distance to the cloud complex, 1 kpc, the model yields an upper limit δκ0em~0.12 cm2 g-1.
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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) | 0.000 | 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) | 0.000 | 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".