A study of the Sunyaev-Zel'dovich increment using archival SCUBA data
Bibliographic record
Abstract
In a search for evidence of the short wavelength increment in the Sunyaev–Zel'dovich (SZ) effect, we have analysed archival galaxy cluster data from the Submillimetre Common User Bolometer Array (SCUBA) on the James Clerk Maxwell Telescope, resulting in the most complete pointed survey of clusters at 850 μm to date. SCUBA's 850-μm passband overlaps the peak of the SZ increment. The sample consists of 44 galaxy clusters in the range 0 < z < 1.3. Maps of each of the clusters have been made and sources have been extracted; as an ancillary product, we generate the most thorough galaxy cluster point source list yet from SCUBA. 17 of these clusters are free of obvious active galactic nuclei (AGN) and have data deep enough to provide interesting measurements of the expected SZ signal. Specialized analysis techniques are employed to extract the SZ effect signal from these SCUBA data, including using SCUBA's short wavelength band as an atmospheric monitor and fitting the long wavelength channel to a model of the spatial distribution of each cluster's SZ effect. By explicitly excising the exact cluster centre from our analysis, we demonstrate that emission from galaxies within the cluster does not contaminate our measurement. The SZ amplitudes from our measurements are consistently higher than the amplitudes inferred from low-frequency measurements of the SZ decrement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".