<i>Planck</i>early results. XII. Cluster Sunyaev-Zeldovich optical scaling relations
Bibliographic record
Abstract
We present the Sunyaev-Zeldovich (SZ) signal-to-richness scaling relation (Y500 − N200) for the MaxBCG cluster catalogue. Employing a multi-frequency matched filter on the Planck sky maps, we measure the SZ signal for each cluster by adapting the filter according to weak-lensing calibrated mass-richness relations (N200 − M500). We bin our individual measurements and detect the SZ signal down to the lowest richness systems (N200 = 10) with high significance, achieving a detection of the SZ signal in systems with mass as low as M500 ≈ 5 × 1013 M⊙. The observed Y500 − N200 relation is well modeled by a power law over the full richness range. It has a lower normalisation at given N200 than predicted based on X-ray models and published mass-richness relations. An X-ray subsample, however, does conform to the predicted scaling, and model predictions do reproduce the relation between our measured bin-average SZ signal and measured bin-average X-ray luminosities. At fixed richness, we find an intrinsic dispersion in the Y500 − N200 relation of 60% rising to of order 100% at low richness. Thanks to its all-sky coverage, Planck provides observations for more than 13000 MaxBCG clusters and an unprecedented SZ/optical data set, extending the list of known cluster scaling laws to include SZ-optical properties. The data set offers essential clues for models of galaxy formation. Moreover, the lower normalisation of the SZ-mass relation implied by the observed SZ-richness scaling has important consequences for cluster physics and cosmological studies with SZ clusters.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".