Mock Observatory: two thousand lightcone mock catalogues of luminous red galaxies from the Hyper Suprime-Cam Survey for the cosmological large-scale analysis
Bibliographic record
Abstract
Estimating a reliable covariance matrix for correlation functions of galaxies is a crucial task to obtain accurate cosmological constraints from galaxy surveys. We generate 2,000 independent lightcone mock luminous red galaxy (LRGs) catalogues at $0.3 \leq z \leq 1.25$, designed to cover CAMIRA LRGs observed by the Subaru Hyper Suprime-Cam Subaru Strategic Programme (HSC SSP). We first produce full-sky lightcone halo catalogues using a COmoving Lagrangian Acceleration (COLA) technique, and then trim them to match the footprints of the HSC SSP S20A Wide layers. The mock LRGs are subsequently populated onto the trimmed halo catalogues according to the halo occupation distribution model constrained by the observed CAMIRA LRGs. The stellar mass ($M_{\star}$) is assigned to each LRG by the subhalo abundance-matching technique using the observed stellar-mass functions of CAMIRA LRGs. We evaluate photometric redshifts (photo-$z$) of mock LRGs by incorporating the photo-$z$ scatter, which is derived from the observed $M_{\star}$--photo-$z$-scatter relations of the CAMIRA LRGs. We validate the constructed full-sky halo and lightcone LRG mock catalogues by comparing their angular clustering statistics (i.e., power spectra and correlation functions) with those measured from the halo catalogues of full $N$-body simulations and the CAMIRA LRG catalogues from the HSC SSP, respectively. We detect clear signatures of baryon acoustic oscillations (BAOs) from our mock LRGs, whose angular scales are well consistent with theoretical predictions. These results demonstrate that our mock LRGs can be used to evaluate covariance matrices at large scales and provide predictions for the BAO detectability and cosmological constraints.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.003 |
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".