Hundreds of weak lensing shear-selected clusters from the Hyper Suprime-Cam Subaru Strategic Program S19A data
Bibliographic record
Abstract
Abstract We use the Hyper Suprime-Cam Subaru Strategic Program S19A shape catalog to construct weak lensing shear-selected cluster samples. From aperture mass maps covering ∼510 deg2 created using a truncated Gaussian filter, we construct a catalog of 187 shear-selected clusters that correspond to mass map peaks with signal-to-noise ratio larger than 4.7. Most of the shear-selected clusters have counterparts in optically selected clusters, from which we estimate the purity of the catalog to be higher than 95%. The sample can be expanded to 418 shear-selected clusters with the same signal-to-noise ratio cut by optimizing the shape of the filter function and by combining weak lensing mass maps created with several different background galaxy selections. We argue that dilution and obscuration effects of cluster member galaxies can be mitigated by using background source galaxy samples and adopting a filter function with its inner boundary larger than about 2′. The large samples of shear-selected clusters that are selected without relying on any baryonic tracer are useful for detailed studies of cluster astrophysics and cosmology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".