Bibliographic record
Abstract
We present the application of the pseudo-spectrum method to galaxy–galaxy lensing. We derive explicit expressions for the pseudo-spectrum analysis of the galaxy-shear cross-spectrum, which is the Fourier space counterpart of the stacked galaxy–galaxy lensing profile. The pseudo-spectrum method corrects observational issues such as the survey geometry, masks of bright stars and their spikes, and inhomogeneous noise, which distort the spectrum and also mix the E-mode and the B-mode signals. Using ray-tracing simulations in N-body simulations including realistic masks, we confirm that the pseudo-spectrum method successfully recovers the input galaxy-shear cross-spectrum. We also show that the galaxy-shear cross-spectrum has an excess covariance relative to the Gaussian covariance at small scales (k ≳ 1h Mpc−1) where the shot noise is dominated in the Gaussian approximation. We find that the excess is consistent with the expectation from the halo sample variance (HSV), which originates from the matter fluctuations at scales larger than the survey area. We apply the pseudo-spectrum method to the observational data of Canada–France–Hawaii Telescope Lensing survey shear catalogue and three different spectroscopic samples of Sloan Digital Sky Survey Luminous Red Galaxy, and Baryon Oscillation Spectroscopic Survey CMASS and LOWZ galaxies. The galaxy-shear cross-spectra are significantly detected at the level of 7–10σ using the analytic covariance with the HSV contribution included. We also confirm that the observed spectra are consistent with the halo model predictions with the halo occupation distribution parameters estimated from previous work. This work demonstrates the viability of galaxy–galaxy lensing analysis in the Fourier space.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".