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Global shear response is IV-weighted while shear statistics are w_des-weighted

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issue の種類
バグ
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おおむね明確
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活発
技術スタック
python
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data

調査の方向性

Start with src/sp_validation/calibration.py, especially _total_response, R_selection, get_variance_ivweights, and get_calibrate_e_from_cat; compare their weighting with cosmo_val/core.py and cosmo_val/cat_config.yaml. Run the three named regression tests to reproduce the reported mismatches. Done means the response and calibration match the estimator’s weights, including selection effects and weight or cell-migration derivatives, with the variance and recalibration weighting issues addressed.

索引モデルが issue の本文から書いたものです。

説明

The global metacal shear response that calibrates the v1.4.6.3 catalogue is averaged with the inverse-variance weight w (global_R_weight: w, recorded in the released header as MC_PAR_6). Every cosmo_val statistic is weighted by w_des, with shear.R: 1.0. For a fixed selection, the response of a weighted mean sum(w e)/sum(w) is the mean of R_g under that same weight, so the applied response does not match the estimator. On a 6M-row sample of the release, the mean diagonal R_g is 0.7785 unweighted, 0.8037 with w_iv and 0.8273 with w_des. Holding the selection response fixed, a consistently weighted shear response would raise the total response by ~2.9%. Current shear amplitudes are therefore ~2.9% above that counterfactual, and xi ~5.9% above it. This is a conditional normalization shift, not a measured bias against true shear after the published m correction. Whether the image-simulation m = -0.057 absorbs it depends on the estimator that produced that m, whose provenance has not been recovered. The current image-sim workflow (unweighted response and numerator) does not contain this term, but it postdates the published chain. No S8 or B-mode PTE shift has been measured. A uniform rescaling need not move a B-mode PTE, because an empirical covariance rescales with the data.

  • metacal._total_response averages R_g with ns["w"] (src/sp_validation/calibration.py:1087-1096), while R_selection uses unweighted means (:1063-1079). The production configs set global_R_weight: w (e.g. config/calibration/mask_v1.X.6.yaml:110). cosmo_val weights by w_des with R: 1.0 (cosmo_val/cat_config.yaml:521-530, cosmo_val/core.py:597-603).
  • get_variance_ivweights adds the shape-error columns to 2*sigma_eps**2 without squaring them (calibration.py:861-869). They are standard deviations in both ShapePipe generations: pars_err at v1.4.0 (ngmix.py:260-266, 45384c91) and sqrt(g_cov) on develop (ngmix.py:957-960, 6ed93f88). On 791,667 released objects, squaring them lowers the IV-weighted mean R_g by 2.39% (subset, not full catalogue). This interacts with the first item: it does not add to it independently, and fixing it alone widens the w_des/IV gap.
  • get_calibrate_e_from_cat weights the additive bias by w_<weight_type> but averages R_g unweighted (calibration.py:635, :640-643), as does the recipe in docs/source/using_the_catalogues.md:199-212. No pipeline calls it.

Reproduction: src/sp_validation/tests/regression/test_response_weight_mismatch.py: gamma_hat=[0.022 -0.011], gamma=[0.02 -0.01]; R applied diag=[0.75 0.75], <R>_w_des=0.825 (toy weights are assigned; a fixture using the real get_w_des recovers 1.0515x the input shear)
src/sp_validation/tests/regression/test_iv_weight_adds_sigma_to_variance.py: applied global response: got 0.73707, expected 0.76115 from inverse measurement variances
src/sp_validation/tests/regression/test_recalibrator_ignores_weight_for_response.py: weight_type='des': recalibrated patch shear 0.025000 != truth 0.02

Where to fix: develop. calibration.py is identical on develop and merge/develop-into-tomo, and both branches weight v1.4.6.3 statistics by w_des.

Fix: derive the response of the estimator that is actually used. That means the w_des-weighted shear response together with a weighted selection response, including weight and cell-migration derivatives; averaging R_selection with w_des alone is not enough. Square the error columns in get_variance_ivweights, and weight R_g in get_calibrate_e_from_cat. Settling the science impact needs the historical m provenance, a consistently weighted m on the same simulations, and a likelihood rerun.

Claude (Opus 5.5) on behalf of Cail

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