AMICO galaxy clusters in KiDS-DR3: Cosmological constraints from the angular power spectrum and correlation function
Bibliographic record
Abstract
We study the tomographic clustering properties of the photometric cluster catalogue derived from the third data release of the Kilo Degree Survey (KiDS), focusing on the angular correlation function and its spherical harmonic counterpart: the angular power spectrum. We measured the angular correlation function and power spectrum from a sample of 5162 clusters, with an intrinsic richness of λ* ≥ 15, in the photometric redshift range of z ∈ [0.1, 0.6]. We compared our measurements with theoretical models, within the framework of the Λ cold dark matter cosmology. We performed a Markov chain Monte Carlo (MCMC) analysis to constrain the cosmological parameters, Ωm and σ8, as well as the structure growth parameter, S8 ≡ σ8√Ωm/0.3. We adopted Gaussian priors on the parameters of the mass-richness relation, based on the posterior distributions derived from a previous joint analysis of cluster counts and weak-lensing mass measurements carried out on the basis of the same catalogue. From the angular correlation function, we obtained Ωm = 0.32−0.04+0.05, σ8 = 0.77−0.09+0.13, and S8 = 0.80−0.06+0.08, which are in agreement, within 1σ, with the 3D clustering result based on the same cluster sample and with existing complementary studies on other data sets. For the angular power spectrum, we checked the validity of the Poissonian shot noise approximation, also considering the mode-mode coupling induced by the mask. We derived statistically consistent results, in particular, Ωm = 0.24−0.04+0.05 and S8 = 0.93−0.12+0.11; while the constraint on σ8 alone is weaker with respect to the one provided by the angular correlation function, σ8 = 1.01−0.17+0.25. Our results show that the 2D clustering from photometric cluster surveys can provide competitive cosmological constraints with respect to the full 3D clustering statistics. We also demonstrate that they can be successfully applied to ongoing and forthcoming spectrometric and photometric surveys.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".