feat: cluster/group examples on Lenstool-native dPIE default + reference-anchored SLaM scaling

dataset/cluster/a2744/mass.csv

@@ -1,4 +1,4 @@
-galaxy,attr_name,profile_class,y,x,ra,rs,b0,mass_at_200,redshift_object,redshift_source,redshift
-lens_0,mass,dPIEMassSph,-30.7476,14.0880,8.0,20.0,3.0,,,,0.308
-lens_1,mass,dPIEMassSph,-10.6992,-3.8597,8.0,20.0,3.0,,,,0.308
-host_halo,dark,NFWMCRLudlowSph,-30.7476,14.0880,,,,2000000000000000.0,0.308,5.662,0.308
+galaxy,attr_name,profile_class,y,x,sigma,r_core,r_cut,mass_at_200,redshift_object,redshift_source,H0,Om0,redshift
+lens_0,mass,dPIEMassSph,-30.7476,14.0880,290.0,8.0,20.0,,0.308,5.662,67.66,0.30966,0.308
+lens_1,mass,dPIEMassSph,-10.6992,-3.8597,290.0,8.0,20.0,,0.308,5.662,67.66,0.30966,0.308
+host_halo,dark,NFWMCRLudlowSph,-30.7476,14.0880,,,,2000000000000000.0,0.308,5.662,,,0.308

dataset/cluster/a2744/prep.py

@@ -104,17 +104,22 @@ def fetch_tsv(table):
 with open(OUT / "mass.csv", "w", newline="") as f:
     writer = csv.writer(f)
     writer.writerow(
-        ["galaxy", "attr_name", "profile_class", "y", "x", "ra", "rs", "b0",
-         "mass_at_200", "redshift_object", "redshift_source", "redshift"]
+        ["galaxy", "attr_name", "profile_class", "y", "x", "sigma", "r_core", "r_cut",
+         "mass_at_200", "redshift_object", "redshift_source", "H0", "Om0", "redshift"]
     )
+    # sigma is Lenstool's fiducial v_disp (km/s); 290 km/s corresponds to a lens
+    # strength b0 ~ 3.0" at (z_l = 0.308, z_s = 5.662). These are initialization
+    # placeholders — the modeling script promotes sigma/r_core/r_cut to priors.
+    # H0 / Om0 are the (fixed) cosmology constants entering the b0 normalization;
+    # they must be present so they load as fixed values rather than free priors.
     for i, bcg in enumerate((bcg_1, bcg_2)):
         writer.writerow(
             [f"lens_{i}", "mass", "dPIEMassSph", f"{bcg[1]:.4f}", f"{bcg[2]:.4f}",
-             8.0, 20.0, 3.0, "", "", "", 0.308]
+             290.0, 8.0, 20.0, "", 0.308, redshift_source_max, 67.66, 0.30966, 0.308]
         )
     writer.writerow(
         ["host_halo", "dark", "NFWMCRLudlowSph", f"{bcg_1[1]:.4f}", f"{bcg_1[2]:.4f}",
-         "", "", "", 2e15, 0.308, redshift_source_max, 0.308]
+         "", "", "", 2e15, 0.308, redshift_source_max, "", "", 0.308]
     )
 
 with open(OUT / "point.csv", "w", newline="") as f:

dataset/cluster/simple/mass.csv

@@ -1,4 +1,4 @@
-galaxy,attr_name,profile_class,y,x,ra,rs,b0,mass_at_200,redshift_object,redshift_source,redshift
-lens_0,mass,dPIEMassSph,0.0,0.0,8.0,20.0,3.0,,,,0.5
-lens_1,mass,dPIEMassSph,10.0,8.0,5.0,12.0,1.2,,,,0.5
-host_halo,dark,NFWMCRLudlowSph,0.0,0.0,,,,1995262314968882.8,0.5,2.0,0.5
+galaxy,attr_name,profile_class,y,x,sigma,r_core,r_cut,redshift_object,redshift_source,H0,Om0,mass_at_200,redshift
+lens_0,mass,dPIEMassSph,0.0,0.0,330.0,8.0,20.0,0.5,2.0,67.66,0.30966,,0.5
+lens_1,mass,dPIEMassSph,10.0,8.0,210.0,5.0,12.0,0.5,2.0,67.66,0.30966,,0.5
+host_halo,dark,NFWMCRLudlowSph,0.0,0.0,,,,0.5,2.0,,,1995262314968882.8,0.5

dataset/cluster/simple/point_dataset_0.json

@@ -2,50 +2,50 @@
     "type": "instance",
     "class_path": "autolens.point.dataset.PointDataset",
     "arguments": {
-        "time_delays_noise_map": null,
-        "redshift": 1.0,
-        "positions_noise_map": {
+        "time_delays": null,
+        "fluxes_noise_map": null,
+        "positions": {
             "type": "instance",
-            "class_path": "autoarray.structures.arrays.irregular.ArrayIrregular",
+            "class_path": "autoarray.structures.grids.irregular_2d.Grid2DIrregular",
             "arguments": {
                 "values": {
                     "type": "ndarray",
                     "array": [
-                        0.005,
-                        0.005,
-                        0.005
+                        [
+                            -9.15390625,
+                            -19.291166924196176
+                        ],
+                        [
+                            0.03671875,
+                            -0.07352194834211641
+                        ],
+                        [
+                            1.7234375000000002,
+                            23.35201416912914
+                        ]
                     ],
                     "dtype": "float64"
                 }
             }
         },
-        "fluxes_noise_map": null,
+        "name": "point_0",
         "fluxes": null,
-        "positions": {
+        "time_delays_noise_map": null,
+        "positions_noise_map": {
             "type": "instance",
-            "class_path": "autoarray.structures.grids.irregular_2d.Grid2DIrregular",
+            "class_path": "autoarray.structures.arrays.irregular.ArrayIrregular",
             "arguments": {
                 "values": {
                     "type": "ndarray",
                     "array": [
-                        [
-                            -9.16875,
-                            -19.276282112568627
-                        ],
-                        [
-                            0.0359375,
-                            -0.0739730032399208
-                        ],
-                        [
-                            1.8125,
-                            23.341188851581837
-                        ]
+                        0.005,
+                        0.005,
+                        0.005
                     ],
                     "dtype": "float64"
                 }
             }
         },
-        "time_delays": null,
-        "name": "point_0"
+        "redshift": 1.0
     }
 }

dataset/cluster/simple/point_dataset_1.json

@@ -2,50 +2,50 @@
     "type": "instance",
     "class_path": "autolens.point.dataset.PointDataset",
     "arguments": {
-        "time_delays_noise_map": null,
-        "redshift": 2.0,
-        "positions_noise_map": {
+        "time_delays": null,
+        "fluxes_noise_map": null,
+        "positions": {
             "type": "instance",
-            "class_path": "autoarray.structures.arrays.irregular.ArrayIrregular",
+            "class_path": "autoarray.structures.grids.irregular_2d.Grid2DIrregular",
             "arguments": {
                 "values": {
                     "type": "ndarray",
                     "array": [
-                        0.005,
-                        0.005,
-                        0.005
+                        [
+                            -15.98203125,
+                            18.998883350418925
+                        ],
+                        [
+                            0.684375,
+                            -0.5133004737014016
+                        ],
+                        [
+                            13.72578125,
+                            -14.291674436932258
+                        ]
                     ],
                     "dtype": "float64"
                 }
             }
         },
-        "fluxes_noise_map": null,
+        "name": "point_1",
         "fluxes": null,
-        "positions": {
+        "time_delays_noise_map": null,
+        "positions_noise_map": {
             "type": "instance",
-            "class_path": "autoarray.structures.grids.irregular_2d.Grid2DIrregular",
+            "class_path": "autoarray.structures.arrays.irregular.ArrayIrregular",
             "arguments": {
                 "values": {
                     "type": "ndarray",
                     "array": [
-                        [
-                            -15.96796875,
-                            19.00429600919258
-                        ],
-                        [
-                            0.68125,
-                            -0.5133004737014016
-                        ],
-                        [
-                            13.69296875,
-                            -14.308814523048826
-                        ]
+                        0.005,
+                        0.005,
+                        0.005
                     ],
                     "dtype": "float64"
                 }
             }
         },
-        "time_delays": null,
-        "name": "point_1"
+        "redshift": 2.0
     }
 }

dataset/cluster/simple/point_datasets.csv

@@ -1,7 +1,7 @@
 name,y,x,positions_noise,redshift
-point_0,-9.16875,-19.276282112568627,0.005,1.0
-point_0,0.0359375,-0.0739730032399208,0.005,1.0
-point_0,1.8125,23.341188851581837,0.005,1.0
-point_1,-15.96796875,19.00429600919258,0.005,2.0
-point_1,0.68125,-0.5133004737014016,0.005,2.0
-point_1,13.69296875,-14.308814523048826,0.005,2.0
+point_0,-9.15390625,-19.291166924196176,0.005,1.0
+point_0,0.03671875,-0.07352194834211641,0.005,1.0
+point_0,1.7234375000000002,23.35201416912914,0.005,1.0
+point_1,-15.98203125,18.998883350418925,0.005,2.0
+point_1,0.684375,-0.5133004737014016,0.005,2.0
+point_1,13.72578125,-14.291674436932258,0.005,2.0