SCUBA-2 observations of candidate starbursting protoclusters selected by Planck and Herschel-SPIRE
Bibliographic record
Abstract
ABSTRACT We present SCUBA-2 850 $\mathrm{ \mu}$m observations of 13 candidate starbursting protoclusters selected using Planck and Herschel data. The cumulative number counts of the 850 $\mathrm{ \mu}$m sources in 9 of 13 of these candidate protoclusters show significant overdensities compared to the field, with the probability <10−2 assuming the sources are randomly distributed in the sky. Using the 250, 350, 500, and 850 $\mathrm{ \mu}$m flux densities, we estimate the photometric redshifts of individual SCUBA-2 sources by fitting spectral energy distribution templates with an MCMC method. The photometric redshift distribution, peaking at 2 < z < 3, is consistent with that of known z > 2 protoclusters and the peak of the cosmic star formation rate density (SFRD). We find that the 850 $\mathrm{ \mu}$m sources in our candidate protoclusters have infrared luminosities of $L_{\mathrm{IR}}\gtrsim 10^{12}\, \mathrm{L}_{\odot }$ and star formation rates of SFR = (500–1500) M⊙ yr−1. By comparing with results in the literature considering only Herschel photometry, we conclude that our 13 candidate protoclusters can be categorized into four groups: six of them being high-redshift starbursting protoclusters, one being a lower redshift cluster or protocluster, three being protoclusters that contain lensed dusty star-forming galaxies or are rich in 850 $\mathrm{ \mu}$m sources, and three regions without significant Herschel or SCUBA-2 source overdensities. The total SFRs of the candidate protoclusters are found to be comparable or higher than those of known protoclusters, suggesting our sample contains some of the most extreme protocluster population. We infer that cross-matching Planck and Herschel data is a robust method for selecting candidate protoclusters with overdensities of 850 $\mathrm{ \mu}$m sources.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".