Candidate high-z protoclusters among the Planck compact sources, as revealed by Herschel–SPIRE
Bibliographic record
Abstract
By determining the nature of all the Planck compact sources within 808.4 deg 2 of large Herschel surveys, we have identified 27 candidate protoclusters of dusty star-forming galaxies (DSFGs) that are at least 3σ overdense in either 250, 350, or 500 µm sources.We find roughly half of all the Planck compact sources are resolved by Herschel into multiple discrete objects, with the other half remaining unresolved by Herschel.We find a significant difference between versions of the Planck catalogues, with earlier releases hosting a larger fraction of candidate protoclusters and Galactic cirrus than later releases, which we ascribe to a difference in the filters used in the creation of the three catalogues.We find a surface density of DSFG candidate protoclusters of (3.3 ± 0.7) × 10 -2 sources deg -2 , in good agreement with previous similar studies.We find that a Planck colour selection of S 857 /S 545 < 2 works well to select candidate protoclusters, but can miss protoclusters at z < 2. The Herschel colours of individual candidate protocluster members indicate our candidate protoclusters all likely all lie at z > 1.Our candidate protoclusters are a factor of 5 times brighter at 353 GHz than expected from simulations, even in the most conservative estimates.Further observations are needed to confirm whether these candidate protoclusters are physical clusters, multiple protoclusters along the line of sight, or chance alignments of unassociated 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".