SCUBA-2: iterative map-making with the Sub-Millimetre User Reduction Facility
Bibliographic record
Abstract
The Submillimetre Common User Bolometer Array 2 (SCUBA-2) is an instrument operating on the 15-m James Clerk Maxwell Telescope, nominally consisting of 5120 bolometers in each of two simultaneous imaging bands centred over 450 and 850 um. The camera is operated by scanning across the sky and recording data at a rate of 200 Hz. As the largest of a new generation of multiplexed kilopixel bolometer cameras operating in the (sub)millimetre, SCUBA-2 data analysis represents a significant challenge. We describe the production of maps using the Sub-Millimetre User Reduction Facility (SMURF) in which we have adopted a fast, iterative approach to map-making that enables data reduction on single, modern, high-end desktop computers, with execution times that are typically shorter than the observing times. SMURF is used in an automated setting, both at the telescope for real-time feedback to observers, as well as for the production of science products for the JCMT Science Archive at the Canadian Astronomy Data Centre. Three detailed case studies are used to: (i) explore convergence properties of the map-maker using simple prior constraints (Uranus -- a point source); (ii) achieve the white-noise limit for faint point-source studies (extragalactic blank-field survey of the Lockman Hole); and (iii) demonstrate that our strategy is capable of recovering angular scales comparable to the size of the array footprint (approximately 5 arcmin) for bright extended sources (star-forming region M17).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".