Reconstructing the density and temperature structure of prestellar cores from $Herschel$ data: A case study for B68 and L1689B
Bibliographic record
Abstract
Utilizing multi-wavelength dust emission maps acquired with $Herschel$, we reconstruct local volume density and dust temperature profiles for the prestellar cores B68 and L1689B using inverse-Abel transform based technique. We present intrinsic radial dust temperature profiles of starless cores directly from dust continuum emission maps disentangling the effect of temperature variations along the line of sight which was previously limited to the radiative transfer calculations. The reconstructed dust temperature profiles show a significant drop in core center, a flat inner part, and a rising outward trend until the background cloud temperature is reached. The central beam-averaged dust temperatures obtained for B68 and L1689B are 9.3 $\pm$ 0.5 K and 9.8 $\pm$0.5 K, respectively, which are lower than the temperatures of 11.3 K and 11.6 K obtained from direct SED fitting. The best mass estimates derived by integrating the volume density profiles of B68 and L1689B are 1.6 M_sol and 11 M_sol, respectively. Comparing our results for B68 with the near-infrared extinction studies, we find that the dust opacity law adopted by the HGBS project, $κ_λ =0.1(λ/300 μm)^{-2}$, agrees to within 50% with the dust extinction constraints
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".