KiDS-1000 and DES-Y1 combined: cosmology from peak count statistics
Bibliographic record
Abstract
ABSTRACT We analyse the fourth data release of the Kilo Degree Survey (KiDS-1000) and extract cosmological parameter constraints based on the cosmic shear peak count statistics. Peaks are identified in aperture mass maps in which the filter is maximally sensitive to angular scales in the range 2–4 arcmin, probing deep into the non-linear regime of structure formation. We interpret our results with a simulation-based inference pipeline, sampling over a broad wCDM prior volume and marginalizing over uncertainties on shape calibration, photometric redshift distribution, intrinsic alignment, and baryonic feedback. Our measurements constrain the structure growth parameter and the amplitude of the non-linear intrinsic alignment model to $\Sigma _8 \equiv \sigma _8\left[\Omega _{\rm m}/0.3\right]^{0.60}=0.765^{+0.030}_{-0.030}$ and $A_{\rm IA}= 0.71^{+0.42}_{-0.42}$, respectively, in agreement with previous KiDS-1000 results based on two-point shear statistics. These results are robust against modelling of the non-linear physics, different scale cuts, and selections of tomographic bins. The posterior is also consistent with that from the Dark Energy Survey Year-1 peak count analysis presented in Harnois-Déraps et al., and hence we jointly analyse both surveys with a common pipeline. We obtain $\Sigma _8^{\rm joint} \equiv \sigma _8\left[\Omega _{\rm m}/0.3\right]^{0.57}=0.759^{+0.020}_{-0.017}$, in agreement with the PlanckwCDM results. The shear-CMB tension on this parameter increases to $3.1\sigma$ when forcing $w=-1.0$, and to $4.1\sigma$ if comparing instead with $S_{8,\Lambda {\rm CDM}}^{\rm joint} = 0.736^{+0.016}_{-0.018}$, one of the tightest constraints to date on this quantity. Residual biases in the photometric redshifts of the DES-Y1 data and in the modelling of small scales physics could lower this tension, however it is robust against other systematics. Limits in the accuracy of our emulator prevent us from constraining $\Omega _{\rm m}$.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".