The JCMT Gould Belt Survey: SCUBA-2 Data Reduction Methods and Gaussian Source Recovery Analysis
Bibliographic record
Abstract
Abstract The James Clerk Maxwell Telescope (JCMT) Gould Belt Survey (GBS) was one of the first legacy surveys with the JCMT in Hawaii, mapping 47 deg2 of nearby (<500 pc) molecular clouds in dust continuum emission at 850 and 450 μm, as well as a more limited area in lines of various CO isotopologues. While molecular clouds and the material that forms stars have structures on many size scales, their larger-scale structures are difficult to observe reliably in the submillimeter regime using ground-based facilities. In this paper, we quantify the extent to which three subsequent data reduction methods employed by the JCMT GBS accurately recover emission structures of various size scales, in particular, dense cores, which are the focus of many GBS science goals. With our current best data reduction procedure, we expect to recover 100% of structures with Gaussian σ sizes of ≤30″ and intensity peaks of at least five times the local noise for isolated peaks of emission. The measured sizes and peak fluxes of these compact structures are reliable (within 15% of the input values), but source recovery and reliability both decrease significantly for larger emission structures and fainter peaks. Additional factors such as source crowding have not been tested in our analysis. The most recent JCMT GBS data release includes pointing corrections, and we demonstrate that these tend to decrease the sizes and increase the peak intensities of compact sources in our data set, mostly at a low level (several percent), but occasionally with notable improvement.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".