Improving photometric galaxy clustering constraints with cross-bin correlations
Bibliographic record
Abstract
Clustering studies in current photometric galaxy surveys focus solely on autocorrelations, neglecting cross-correlations between redshift bins. We evaluate the potential advantages and drawbacks of incorporating cross-bin correlations in Fisher forecasts for the Dark Energy Survey (DES) and the forthcoming Rubin Observatory Legacy Survey of Space and Time (LSST). Our analysis considers the impact of including redshift space distortions (RSD) and magnification in model predictions, as well as systematic uncertainties in photometric redshift distributions (photo-$z$). While autocorrelations alone suffer from a degeneracy between the amplitude of matter fluctuations (${\ensuremath{\sigma}}_{8}$) and galaxy bias parameters, accounting for RSD and magnification in cross-correlations helps break this degeneracy---although more weakly than the degeneracy breaking expected from a combined analysis with other observables. Incorporating cross-bin correlations does not significantly increase sensitivity to photo-$z$ systematics, addressing previous concerns, and self-calibrates photo-$z$ systematics, reducing errors on photo-$z$ nuisance parameters. We suggest that the benefits of including cross-correlations in future photometric galaxy clustering analyses outweigh the risks, but caution that careful evaluation is necessary as more realistic pictures of surveys' precision and systematic error budgets develop.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".