Overdensities of submillimetre-bright sources around candidate protocluster cores selected from the South Pole Telescope survey
Bibliographic record
Abstract
ABSTRACT We present APEX-LABOCA 870-μm observations of the fields surrounding the nine brightest high-redshift unlensed objects discovered in the South Pole Telescope’s (SPT) 2500 deg2 survey. Initially seen as point sources by SPT’s 1-arcmin beam, the 19-arcsec resolution of our new data enables us to deblend these objects and search for submillimetre (submm) sources in the surrounding fields. We find a total of 98 sources above a threshold of 3.7σ in the observed area of 1300 arcmin2, where the bright central cores resolve into multiple components. After applying a radial cut to our LABOCA sources to achieve uniform sensitivity and angular size across each of the nine fields, we compute the cumulative and differential number counts and compare them to estimates of the background, finding a significant overdensity of $\delta \, {\approx }\,$10 at $S_{870}= 14$ mJy. The large overdensities of bright submm sources surrounding these fields suggest that they could be candidate protoclusters undergoing massive star formation events. Photometric and spectroscopic redshifts of the unlensed central objects range from $z= $3 to 7, implying a volume density of star-forming protoclusters of approximately 0.1 Gpc−3. If the surrounding submm sources in these fields are at the same redshifts as the central objects, then the total star formation rates of these candidate protoclusters reach 10 000 M⊙ yr−1, making them much more active at these redshifts than seen so far in either simulations or observations.
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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 | 0.000 |
| 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".