Candidate high-redshift protoclusters and lensed galaxies in the <i>Planck</i> list of high-<i>z</i> sources overlapping with <i>Herschel</i>-SPIRE imaging
Bibliographic record
Abstract
ABSTRACT The Planck list of high-redshift source candidates (the PHz catalogue) contains 2151 peaks in the cosmic infrared background, unresolved by Planck’s 5 arcmin beam. Follow-up spectroscopic observations have revealed that some of these objects are $z\, {\approx }\, 2$ protoclusters and strong gravitational lenses but an unbiased survey has not yet been carried out. To this end, we have used archival Herschel-SPIRE observations to study a uniformly selected sample of 187 PHz sources. In contrast with follow-up studies that were biased towards bright, compact sources, we find that only one of our PHz sources is a bright gravitationally lensed galaxy (peak flux ${\gtrsim }\, 300$ mJy), indicating that such objects are rarer in the PHz catalogue than previously believed (<1 per cent). The majority of our PHz sources consist of many red, star-forming galaxies, demonstrating that typical PHz sources are candidate protoclusters. However, our new PHz sources are significantly less bright than found in previous studies and differ in colour, suggesting possible differences in redshift and star formation rate. None the less, 40 of our PHz sources contain ${\gt }\, 3\, \sigma$ galaxy overdensities, comparable to the fraction of ${\gt }\, 3\, \sigma$ overdensities found in earlier biased studies. We additionally use a machine-learning approach to identify less extreme (peak flux ${\sim }\, 100$ mJy) gravitationally lensed galaxies among Herschel-SPIRE observations of PHz sources, finding a total of seven candidates in our unbiased sample, and 13 amongst previous biased samples. Our new uniformly selected catalogues of ${\gt }\, 3\, \sigma$ candidate protoclusters and strong gravitational lenses provide interesting targets for follow up with higher resolution facilities, such as ALMA and JWST.
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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) | 0.004 | 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".