An optically-selected cluster catalog at redshift 0.1 &lt; <i>z</i> &lt; 1.1 from the Hyper Suprime-Cam Subaru Strategic Program S16A data
Bibliographic record
Abstract
Abstract We present an optically-selected cluster catalog from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The HSC images are sufficiently deep to detect cluster member galaxies down to M* ∼ 1010.2 M⊙ even at z ∼ 1, allowing a reliable cluster detection at such high redshifts. We apply the CAMIRA algorithm to the HSC Wide S16A dataset covering ∼232 deg2 to construct a catalog of 1921 clusters at redshift 0.1 &lt; z &lt; 1.1 and richness ${\skew7\hat{N}}_{\rm mem}&gt;15$ that roughly corresponds to M200m ≳ 1014 h−1 M⊙. We confirm good cluster photometric redshift performance, with the bias and the scatter in Δz/(1 + z) being better than 0.005 and 0.01, respectively, over most of the redshift range. We compare our cluster catalog with large X-ray cluster catalogs from the XXL and XMM-LSS (the XMM Large Scale Structure) surveys and find good correlation between richness and X-ray properties.We also study the mis-centering effect from the distribution of offsets between optical and X-ray cluster centers. We confirm the high (&gt;0.9) completeness and purity for high-mass clusters by analyzing mock galaxy catalogs.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| 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".