THE IDENTIFICATION OF FILAMENTS ON FAR-INFRARED AND SUBMILLIMITER IMAGES: MORPHOLOGY, PHYSICAL CONDITIONS AND RELATION WITH STAR FORMATION OF FILAMENTARY STRUCTURE
Bibliographic record
Abstract
Observations of molecular clouds reveal a complex structure, with gas and dust often arranged in filamentary, rather than spherical geometries. The association of pre- and proto-stellar cores with the filaments suggests a direct link with the process of star formation. Any study of the properties of such filaments requires representative samples from different environments for an unbiased detection method. We developed such an approach using the Hessian matrix of a surface-brightness distribution to identify filaments and determine their physical and morphological properties. After testing the method on simulated, but realistic, filaments, we apply the algorithms to column-density maps computed from Herschel observations of the Galactic plane obtained by the Hi-GAL project. We identified ∼500 filaments, in the longitude range of l = 216 5 to l = 225 5, with lengths from ∼1 pc up to ∼30 pc and widths between 0.1 pc and 2.5 pc. Average column densities are between 10 20 cm −2 and 10 22 cm −2 . Filaments include the majority of dense material with > 6 × 10 21 cm −2 . We find that the pre- and proto-stellar compact sources already identified in the same region are mostly associated with filaments. However, surface densities in excess of the expected critical values for high-mass star formation are only found on the filaments, indicating that these structures are necessary to channel material into the clumps. Furthermore, we analyze the gravitational stability of filaments and discuss their relationship with star formation.
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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) | 0.001 | 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".