THE HERSCHEL AND JCMT GOULD BELT SURVEYS: CONSTRAINING DUST PROPERTIES IN THE PERSEUS B1 CLUMP WITH PACS, SPIRE, AND SCUBA-2
Bibliographic record
Abstract
We present Herschel observations from the Herschel Gould Belt Survey and SCUBA-2 science verification observations from the JCMT Gould Belt Survey of the B1 clump in the Perseus molecular cloud. We determined the dust emissivity index using four different techniques to combine the Herschel PACS+SPIRE data at 160–500 μm with the SCUBA-2 data at 450 μm and 850 μm. Of our four techniques, we found that the most robust method was filtering out the large-scale emission in the Herschel bands to match the spatial scales recovered by the SCUBA-2 reduction pipeline. Using this method, we find β ≈ 2 toward the filament region and moderately dense material and lower β values (β ≳ 1.6) toward the dense protostellar cores, possibly due to dust grain growth. We find that β and temperature are more robust with the inclusion of the SCUBA-2 data, improving estimates from Herschel data alone by factors of ∼2 for β and by ∼40% for temperature. Furthermore, we find core mass differences of ≲ 30% compared to Herschel -only estimates with an adopted β = 2, highlighting the necessity of long-wavelength submillimeter data for deriving accurate masses of prestellar and protostellar cores.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".