Store shape dispersion per component

cosmo_val/cat_config.yaml

@@ -1,5 +1,7 @@
 # One entry per catalogue. `blind:` is `none` (public) or the name of the blind
 # its signal is concealed under (sp_validation.blinding).
+# cov_th.sigma_e is the per-component dispersion. DES and SP_axel_v0.0
+# preserve their former OneCovariance inputs; their original convention is unknown.
 DES:
   blind: none
   subdir: /n17data/mkilbing/astro/data/DES
@@ -12,7 +14,7 @@ DES:
     A: 4143
     n_e: 5.67
     n_psf: 3.8
-    sigma_e: 0.29
+    sigma_e: 0.20506096654409875
   psf:
     PSF_size: piff_T
     star_size: obs_T
@@ -54,7 +56,7 @@ SP_axel_v0.0:
     A: 1500
     n_e: 6.47
     n_psf: 0.47
-    sigma_e: 0.3
+    sigma_e: 0.21213203435596423
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -169,7 +171,7 @@ SP_v1.3.6:
     A: 2894.0303815287743
     n_e: 4.002931781928292
     n_psf: 0.752316232272063
-    sigma_e: 0.3785853255980945
+    sigma_e: 0.2677002509881296
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -214,7 +216,7 @@ SP_v1.4.5:
     A: 2894.0303815287743
     n_e: 6.477062501504287
     n_psf: 0.4973044366748657
-    sigma_e: 0.39740419232655594
+    sigma_e: 0.2810071992660706
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -259,7 +261,7 @@ SP_v1.4.5.A:
     A: 2420.2651014497287
     n_e: 7.040818382014773
     n_psf: 0.752316232272063
-    sigma_e: 0.30961528707207325
+    sigma_e: 0.21893106904768259
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -303,7 +305,7 @@ SP_v1.4.5_bright:
     A: 2420.2651014497287
     n_e: 7.040818382014773
     n_psf: 0.752316232272063
-    sigma_e: 0.30961528707207325
+    sigma_e: 0.21893106904768259
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -345,7 +347,7 @@ SP_v1.4.5_faint:
     A: 2420.2651014497287
     n_e: 7.040818382014773
     n_psf: 0.752316232272063
-    sigma_e: 0.30961528707207325
+    sigma_e: 0.21893106904768259
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -387,7 +389,7 @@ SP_v1.4.6_glass_mock:
     A: 2405.3892055695346
     n_e: 6.128201234871523
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -431,7 +433,7 @@ SP_v1.4.5_intermediate:
     A: 2420.2651014497287
     n_e: 7.040818382014773
     n_psf: 0.752316232272063
-    sigma_e: 0.30961528707207325
+    sigma_e: 0.21893106904768259
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -473,7 +475,7 @@ SP_v1.4.6:
     A: 2894.0303815287743
     n_e: 5.09348802763124
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -519,7 +521,7 @@ SP_v1.4.6.3:
     A: 2894.0303815287743
     n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.3783979556382064
+    sigma_e: 0.2675677604189021
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -564,7 +566,7 @@ SP_v1.4.6.3_B:
     A: 2894.0303815287743
     n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.3783979556382064
+    sigma_e: 0.2675677604189021
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -609,7 +611,7 @@ SP_v1.4.6.3_C:
     A: 2894.0303815287743
     n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.3783979556382064
+    sigma_e: 0.2675677604189021
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -654,7 +656,7 @@ SP_v1.4.6.3_A:
     A: 2894.0303815287743
     n_e: 4.957279270321334
     n_psf: 0.752316232272063
-    sigma_e: 0.3783979556382064
+    sigma_e: 0.2675677604189021
   psf:
     PSF_flag: HSM_FLAG_PSF
     PSF_size: HSM_T_PSF
@@ -698,7 +700,7 @@ SP_v1.4.6_ecut07:
     A: 2894.0303815287743
     n_e: 4.77419733499695
     n_psf: 0.752316232272063
-    sigma_e: 0.34187904441900674
+    sigma_e: 0.24174499065425653
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -743,7 +745,7 @@ SP_v1.4.6.1:
     A: 2405.3892055695346
     n_e: 6.128201234871523
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -788,7 +790,7 @@ SP_v1.4.7:
     A: 2050.5970269635545
     n_e: 6.121543643587931
     n_psf: 0.752316232272063
-    sigma_e: 0.3797419081703805
+    sigma_e: 0.26851807836799524
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -835,7 +837,7 @@ SP_v1.4.8:
     A: 2517.021356326454
     n_e: 4.983323636055024
     n_psf: 0.752316232272063
-    sigma_e: 0.3797397825805285
+    sigma_e: 0.26851657534899687
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_starhalo_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -880,7 +882,7 @@ SP_v1.4.11.2:
     A: 2894.0303815287743
     n_e: 6.2581259680050625
     n_psf: 0.752316232272063
-    sigma_e: 0.380499698062307
+    sigma_e: 0.2690539167392911
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -925,7 +927,7 @@ SP_v1.4.11.3:
     A: 2894.0303815287743
     n_e: 6.2581259680050625
     n_psf: 0.752316232272063
-    sigma_e: 0.380499698062307
+    sigma_e: 0.2690539167392911
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -970,7 +972,7 @@ SP_v1.4.12.3:
     A: 2405.3892055695346
     n_e: 6.128201234871523
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -1015,7 +1017,7 @@ SP_v1.4.13.3:
     A: 2405.3892055695346
     n_e: 6.128201234871523
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -1060,7 +1062,7 @@ SP_v1.4.11.3_ecut07:
     A: 2894.0303815287743
     n_e: 5.830114732534133
     n_psf: 0.752316232272063
-    sigma_e: 0.34058513153783426
+    sigma_e: 0.24083005608171487
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -1210,7 +1212,7 @@ SP_v1.6.6:
     A: 2405.3892055695346
     n_e: 6.128201234871523
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.fits
   psf:
     PSF_flag: HSM_FLAG_PSF
@@ -1257,7 +1259,7 @@ GLASS_mock_validation:
     A: 2405.3892055695346
     n_e: 6.128201234871523
     n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
+    sigma_e: 0.26840896706663525
   mask: /n09data/guerrini/glass_mock_test/mask_nside_1024.fits
   psf:
     PSF_flag: HSM_FLAG_PSF

src/sp_validation/survey.py

@@ -241,9 +241,8 @@ def ellipticity_dispersion(e1, e2, w):
     """Per-component weighted ellipticity dispersion.
 
     Computes ``sqrt(0.5 · (⟨e1²⟩ + ⟨e2²⟩))`` where each component average is
-    weighted by ``w²``. This is the *per-component* RMS convention; it differs by
-    a factor ``√2`` from the summed-component ``sigma_e`` returned by
-    :func:`effective_survey_stats`. Keep the two conventions distinct.
+    weighted by ``w²``. This is the same per-component RMS convention as
+    :func:`effective_survey_stats`.
 
     Parameters
     ----------
@@ -298,10 +297,8 @@ def additive_bias(e1, e2, w, R):
 def effective_survey_stats(e1, e2, w, area_deg2):
     """Effective survey statistics for a shear catalogue.
 
-    Bundles the area-dependent number density and the summed-component shape
-    noise. Here ``sigma_e = sqrt(Σ[w²(e1² + e2²)] / Σw²)`` sums over *both*
-    components (no ``0.5`` factor); this is distinct from the per-component
-    :func:`ellipticity_dispersion`, differing by a factor ``√2``.
+    Bundles the area-dependent number density and per-component shape noise:
+    ``sigma_e = sqrt(Σ[w²(e1² + e2²)] / (2 Σw²))``.
 
     Parameters
     ----------
@@ -325,7 +322,7 @@ def effective_survey_stats(e1, e2, w, area_deg2):
     sum_w2 = float(np.sum(w**2))
     sum_w2_e2 = float(np.sum((w**2) * (e1**2 + e2**2)))
 
-    sigma_e = np.sqrt(sum_w2_e2 / sum_w2) if sum_w2 > 0 else 0.0
+    sigma_e = np.sqrt(sum_w2_e2 / (2 * sum_w2)) if sum_w2 > 0 else 0.0
 
     return {
         "n_eff": n_eff_density(w, area_deg2),

src/sp_validation/tests/test_sigma_e_convention.py

@@ -0,0 +1,41 @@
+"""Regression tests for the per-component shape-noise convention."""
+
+import ast
+from pathlib import Path
+
+import numpy as np
+import pytest
+import yaml
+
+from sp_validation.survey import effective_survey_stats, ellipticity_dispersion
+
+ROOT = Path(__file__).resolve().parents[3]
+
+
+@pytest.mark.parametrize(
+    ("version", "old_sigma"),
+    [("SP_v1.4.6.3", 0.3783979556382064), ("DES", 0.29), ("SP_axel_v0.0", 0.3)],
+)
+def test_config_preserves_covariance_inputs(version, old_sigma):
+    config = yaml.safe_load((ROOT / "cosmo_val/cat_config.yaml").read_text())
+    entry = config[version]
+    # Exercise the actual Snakemake parameter function without importing Snakemake.
+    source = (ROOT / "workflow/rules/covariance.smk").read_text()
+    function = source[source.index("def get_onecov_cat_params(") :]
+    function = function[: function.index("\n\ndef ")]
+    namespace = {"catalogue_entry": lambda _: entry}
+    exec(compile(ast.parse(function), "covariance.smk", "exec"), namespace)
+    sigma, density, _ = namespace["get_onecov_cat_params"](version)
+    sigma = float(sigma)
+    assert sigma == pytest.approx(old_sigma / np.sqrt(2))
+    assert sigma**2 / density == pytest.approx(old_sigma**2 / (2 * density))
+    # CosmoCov's summed-component input, when constructed from this value.
+    assert sigma * np.sqrt(2) == pytest.approx(old_sigma)
+
+
+def test_survey_stats_per_component():
+    e1 = np.array([0.1, 0.4, -0.3])
+    e2 = np.array([0.2, -0.1, 0.5])
+    weights = np.array([1.0, 2.0, 3.0])
+    stats = effective_survey_stats(e1, e2, weights, 1.0)
+    assert stats["sigma_e"] == pytest.approx(ellipticity_dispersion(e1, e2, weights))

workflow/rules/covariance.smk

@@ -20,10 +20,8 @@ def get_onecov_cat_params(version):
     catalogue entry. Tomographic values are comma-separated strings, one entry
     per bin."""
     cov_th = catalogue_entry(version)["cov_th"]
-    # cov_th.sigma_e sums both components; OneCovariance takes it per component.
-    sigma_e = ", ".join(
-        repr(float(s) / 2**0.5) for s in str(cov_th["sigma_e"]).split(",")
-    )
+    # Both cov_th.sigma_e and OneCovariance use per-component dispersion.
+    sigma_e = ", ".join(repr(float(s)) for s in str(cov_th["sigma_e"]).split(","))
     return sigma_e, cov_th["n_e"], cov_th["A"]