SCUBA-2: the 10 000 pixel bolometer camera on the James Clerk Maxwell Telescope
Bibliographic record
Abstract
Submillimetre Common-User Bolometer Array 2 (SCUBA-2) is an innovative 10 000 pixel bolometer camera operating at submillimetre wavelengths on the James Clerk Maxwell Telescope (JCMT). The camera has the capability to carry out wide-field surveys to unprecedented depths, addressing key questions relating to the origins of galaxies, stars and planets. With two imaging arrays working simultaneously in the atmospheric windows at 450 and 850 μm, the vast increase in pixel count means that SCUBA-2 maps the sky 100–150 times faster than the previous SCUBA instrument. In this paper, we present an overview of the instrument, discuss the physical characteristics of the superconducting detector arrays, outline the observing modes and data acquisition, and present the early performance figures on the telescope. We also showcase the capabilities of the instrument via some early examples of the science SCUBA-2 that have already been undertaken. In 2012 February, SCUBA-2 began a series of unique legacy surveys for the JCMT community. These surveys will take 2.5 yr and the results are already providing complementary data to the shorter wavelength, shallower, larger area surveys from Herschel. The SCUBA-2 surveys will also provide a wealth of information for further study with new facilities such as ALMA, and future telescopes such as CCAT and SPICA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".