The JCMT and<i>Herschel</i>Gould Belt Surveys: a comparison of SCUBA-2 and<i>Herschel</i>data of dense cores in the Taurus dark cloud L1495
Bibliographic record
Abstract
We present a comparison of Submillimetre Common User Bolometer Array-2 (SCUBA-2) 850-μm and Herschel 70–500-μm observations of the L1495 filament in the Taurus Molecular Cloud with the goal of characterizing the SCUBA-2 Gould Belt Survey (GBS) data set. We identify and characterize starless cores in three data sets: SCUBA-2 850-μm, Herschel 250-μm, and Herschel 250-μm spatially filtered to mimic the SCUBA-2 data. SCUBA-2 detects only the highest-surface-brightness sources, principally detecting protostellar sources and starless cores embedded in filaments, while Herschel is sensitive to most of the cloud structure, including extended low-surface-brightness emission. Herschel detects considerably more sources than SCUBA-2 even after spatial filtering. We investigate which properties of a starless core detected by Herschel determine its detectability by SCUBA-2, and find that they are the core's temperature and column density (for given dust properties). For similar-temperature cores, such as those seen in L1495, the surface brightnesses of the cores are determined by their column densities, with the highest-column-density cores being detected by SCUBA-2. For roughly spherical geometries, column density corresponds to volume density, and so SCUBA-2 selects the densest cores from a population at a given temperature. This selection effect, which we quantify as a function of distance, makes SCUBA-2 ideal for identifying those cores in Herschel catalogues that are closest to forming stars. Our results can now be used by anyone wishing to use the SCUBA-2 GBS data set.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.000 |
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".