<i>Spitzer</i>-selected <i>z</i> &gt; 1.3 protocluster candidates in the LSST Deep Drilling Fields
Bibliographic record
Abstract
ABSTRACT We have identified 189 candidate z > 1.3 protoclusters and clusters in the LSST Deep Drilling Fields. This sample will enable the measurement of the metal enrichment and star formation history of clusters during their early assembly period through the direct measurement of the rate of supernovae identified through the LSST. The protocluster sample was selected from galaxy overdensities in a Spitzer/IRAC colour-selected sample using criteria that were optimized for protocluster purity using a realistic light-cone. Our tests reveal that $60\!-\!80~{{\ \rm per\ cent}}$ of the identified candidates are likely to be genuine protoclusters or clusters, which is corroborated by a ∼4σ stacked X-ray signal from these structures. We provide photometric redshift estimates for 47 candidates which exhibit strong peaks in the photo-z distribution of their candidate members. However, the lack of a photo-z peak does not mean a candidate is not genuine, since we find a stacked X-ray signal of similar significance from both the candidates that exhibit photo-z peaks and those that do not. Tests on the light-cone reveal that our pursuit of a pure sample of protoclusters results in that sample being highly incomplete ($\sim 4~{{\ \rm per\ cent}}$) and heavily biased towards larger, richer, more massive, and more centrally concentrated protoclusters than the total protocluster population. Most ($\sim 75~{{\ \rm per\ cent}}$) of the selected protoclusters are likely to have a maximum collapsed halo mass of between 1013 and 1014 M⊙, with only $\sim 25~{{\ \rm per\ cent}}$ likely to be collapsed clusters above 1014 M⊙. However, the aforementioned bias ensures our sample is $\sim 50~{{\ \rm per\ cent}}$ complete for structures that have already collapsed into clusters more massive than 1014 M⊙.
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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) | 0.002 | 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".