6 × 2 pt: Forecasting gains from joint weak lensing and galaxy clustering analyses with spectroscopic-photometric galaxy cross-correlations
Bibliographic record
Abstract
Accurate knowledge of galaxy redshift distributions is crucial in the inference of cosmological parameters from large-scale structure data. We explore the potential for enhanced self-calibration of photometric galaxy redshift distributions, n(z), through the joint analysis of up to six two-point functions. Our 3 × 2 pt configuration comprises photometric shear, spectroscopic galaxy clustering, and spectroscopic-photometric galaxy-galaxy lensing (GGL). We expand this to include spectroscopic-photometric cross-clustering, photometric GGL, and photometric auto-clustering, using the photometric shear sample as an additional density tracer. We performed simulated likelihood forecasts of the cosmological and nuisance parameter constraints for stage-III- and stage-IV-like surveys. For the stage-III-like survey, we employed realistic redshift distributions with perturbations across the full shape of the n(z), and distinguished between ‘coherent’ shifting of the bulk distribution in one direction, versus more internal scattering and full-shape n(z) errors. For perfectly known n(z), a 6 × 2 pt analysis gains ∼40% in figure of merit (FoM) on the S8 ≡ σ8√Ωm/0.3 and Ωm plane relative to the 3 × 2 pt analysis. If untreated, coherent and incoherent redshift errors lead to inaccurate inferences of S8 and Ωm, respectively, and contaminate inferences of the amplitude of intrinsic galaxy alignments. Employing bin-wise scalar shifts, δzi, in the tomographic mean redshifts reduces cosmological parameter biases, with a 6 × 2 pt analysis constraining the δzi parameters with 2 − 4 times the precision of a photometric 3ph × 2 pt analysis. For the stage-IV-like survey, a 6 × 2 pt analysis doubles the FoM (σ8–Ωm) compared to the 3 × 2 pt or 3ph × 2 pt analyses, and is only 8% less constraining than if the n(z) were perfectly known. A Gaussian mixture model for the n(z) is able to reduce mean-redshift errors whilst preserving the n(z) shape, and thereby yields the most accurate and precise cosmological constraints for any given N × 2 pt configuration in the presence of n(z) biases.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".