<i>Spitzer Planck Herschel</i> Infrared Cluster (SPHerIC) survey: Candidate galaxy clusters at 1.3 < <i>z</i> < 3 selected by high star-formation rate
Bibliographic record
Abstract
There is a lack of large samples of spectroscopically confirmed clusters and protoclusters at high redshifts, z > 1.5. Discovering and characterizing distant (proto-)clusters is important for yielding insights into the formation of large-scale structure and on the physical processes responsible for regulating star-formation in galaxies in dense environments. The Spitzer Planck Herschel Infrared Cluster (SPHerIC) survey was initiated to identify these characteristically faint and dust-reddened sources during the epoch of their early assembly. We present Spitzer/IRAC observations of 82 galaxy (proto-)cluster candidates at 1.3 < zp < 3.0 that were vetted in a two step process: (1) using Planck to select by color those sources with the highest star-formation rates, and (2) using Herschel at higher resolution to separate out the individual red sources. The addition of the Spitzer data enables efficient detection of the central and massive brightest red cluster galaxies (BRCGs). We find that BRCGs are associated with highly significant, extended and crowded regions of IRAC sources which are more overdense than the field. This result corroborates our hypothesis that BRCGs within the Planck–Herschel sources trace some of the densest and actively star-forming proto-clusters in the early Universe. On the basis of a richness-mass proxy relation, we obtain an estimate of their mean masses which suggests our sample consists of some of the most massive clusters at z ≈ 2 and are the likely progenitors of the most massive clusters observed today.
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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 | 0.002 | 0.002 |
| 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) | 0.006 | 0.002 |
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".