SCUBA-2 overdensities associated with candidate protoclusters selected from <i>Planck</i> data
Bibliographic record
Abstract
ABSTRACT We measure the 850-μm source densities of 46 candidate protoclusters selected from the Planck high-z catalogue (PHz) and the Planck Catalogue of Compact Sources (PCCS) that were followed up with Herschel-SPIRE and SCUBA-2. This paper aims to search for overdensities of 850-μm sources in order to select the fields that are most likely to be genuine protoclusters. Of the 46 candidate protoclusters, 25 have significant overdensities (>5 times the field counts), 11 have intermediate overdensities (3–5 times the field counts), and 10 have no overdensity (<3 times the field counts) of 850-μm sources. We find that the enhanced number densities are unlikely to be the result of sample variance. Compared with the number counts of another sample selected from Planck’s compact source catalogues, this [PHz + PCCS]-selected sample has a higher fraction of candidate protoclusters with significant overdensities, though both samples show overdensities of 850-μm sources above intermediate level. Based on the estimated star formation rate densities (SFRDs), we suggest that both samples can efficiently select protoclusters with starbursting galaxies near the redshift at which the global field SFRD peaks (2 < z < 3). Based on the confirmation of overdensities found here, future follow-up observations on other PHz targets may greatly increase the number of genuine dusty star-forming galaxy-rich clusters/protoclusters.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".