The JCMT Gould Belt Survey: a quantitative comparison between SCUBA-2 data reduction methods
Bibliographic record
Abstract
Performing ground-based submillimetre observations is a difficult task as the measurements are subject to absorption and emission from water vapour in the Earth's atmosphere and time variation in weather and instrument stability. Removing these features and other artefacts from the data is a vital process which affects the characteristics of the recovered astronomical structure we seek to study. In this paper, we explore two data reduction methods for data taken with the Submillimetre Common-User Bolometer Array-2 (SCUBA-2) at the James Clerk Maxwell Telescope (JCMT). The JCMT Legacy Reduction 1 (JCMT LR1) and The Gould Belt Legacy Survey Legacy Release 1 (GBS LR1) reduction both use the same software (starlink) but differ in their choice of data reduction parameters. We find that the JCMT LR1 reduction is suitable for determining whether or not compact emission is present in a given region and the GBS LR1 reduction is tuned in a robust way to uncover more extended emission, which better serves more in-depth physical analyses of star-forming regions. Using the GBS LR1 method, we find that compact sources are recovered well, even at a peak brightness of only three times the noise, whereas the reconstruction of larger objects requires much care when drawing boundaries around the expected astronomical signal in the data reduction process. Incorrect boundaries can lead to false structure identification or it can cause structure to be missed. In the JCMT LR1 reduction, the extent of the true structure of objects larger than a point source is never fully recovered.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".