Cosmological inference from combining <i>Planck</i> and ACT cluster counts
Bibliographic record
Abstract
ABSTRACT We have adapted the Planck cluster likelihood in such a way that it can be applied to the sample of clusters detected by the Atacama Cosmology Telescope (ACT). Applying it to the Planck sample from 2016 and the ACT sample from 2018, we find, by fixing the cosmology using cosmic microwave background observations and the cluster model adopted by Planck, that the mass bias required by the two are $1-b_{\rm Planck}=0.61\pm 0.03$ and $1-b_{\rm ACT}=0.75\pm 0.06$. These are broadly in agreement but hint that the model could be adapted to reach a better agreement. By normalizing the cluster model using weak lensing observations, we find evidence for either evolution in the cluster model, quantified by the cluster modelling parameter describing redshift dependence $\beta =0.86 \pm 0.07$ using an updated Canadian Cluster Comparison Project (CCCP)-based normalization, or evolution in the cosmological model quantified by the dark energy equation-of-state parameter $w=-0.82 \pm 0.07$.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".