Dust temperature tracing the ISRF intensity in the Galaxy
Bibliographic record
Abstract
New observations with Herschel allow accurate measurement of the equilibrium temperature of large dust grains heated by the interstellar radiation field (ISRF), which is critical in deriving dust column density and masses. We present temperature maps derived from the Herschel SPIRE and PACS data in two fields along the Galactic plane, obtained as part of the Hi-GAL survey during the Herschel science demonstration phase (SDP). We analyze the distribution of the dust temperature spatially, as well as along the two lines-of-sight (LOS) through the Galaxy. The zero-level offsets in the Herschel maps were established by comparison with the IRAS and Planck data at comparable wavelengths. We derive maps of the dust temperature and optical depth by adjusting a detailed model for dust emission at each pixel. The dust temperature maps show variations in the ISRF intensity and reveal the intricate mixture of the warm dust heated by massive stars and the cold filamentary structures of embedded molecular clouds. The dust optical depth at 250μm is well correlated with the gas column density, but with a significantly higher dust emissivity than in the solar neighborhood. We correlate the optical depth with 3-D cubes of the dust extinction to investigate variations in the ISRF strength and dust abundance along the line of sight through the spiral structure of the Galaxy. We show that the warmest dust along the LOS is located in the spiral arms of the Galaxy, and we quantify their respective IR contribution.
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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 | 0.000 | 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".