From 50d673269c19de4bc89c2c29d4a41beb0683c986 Mon Sep 17 00:00:00 2001
From: Jammy2211 <JNightingale2211@gmail.com>
Date: Sat, 19 Sep 2026 15:51:59 +0100
Subject: [PATCH] test: pin MassField retention in chained lens models

---
 docs/general/model_cookbook.md                | 17 ++++++++
 .../analysis/test_chaining_fields.py          | 42 +++++++++++++++++++
 2 files changed, 59 insertions(+)
 create mode 100644 test_autolens/analysis/test_chaining_fields.py

diff --git a/docs/general/model_cookbook.md b/docs/general/model_cookbook.md
index 08f16c6ba..09b155187 100644
--- a/docs/general/model_cookbook.md
+++ b/docs/general/model_cookbook.md
@@ -631,6 +631,23 @@ The following example notebooks show how to compose and fit these models:
 
 When performing non-linear search chaining, the inferred model of one phase can be linked to the model.
 
+For a model with a separate external field, carry the mass and field together:
+
+```python
+mass, field = al.util.chaining.mass_and_fields_from(
+    mass=af.Model(al.mp.PowerLaw),
+    mass_result=result_1.model.galaxies.lens.mass,
+    fields_result=result_1.model.fields,
+)
+model_2 = af.Collection(
+    galaxies=af.Collection(lens=af.Model(al.Galaxy, redshift=0.5, mass=mass), source=source),
+    fields=field,
+)
+```
+
+`result_1.model.fields` passes posterior priors into the next stage; use
+`result_1.instance.fields` to fix the field to the previous fit's values.
+
 The following example notebooks show how to compose and fit these models:
 
 <https://github.com/PyAutoLabs/autolens_workspace/blob/main/notebooks/guides/modeling/chaining.ipynb>
diff --git a/test_autolens/analysis/test_chaining_fields.py b/test_autolens/analysis/test_chaining_fields.py
new file mode 100644
index 000000000..af46c6dbb
--- /dev/null
+++ b/test_autolens/analysis/test_chaining_fields.py
@@ -0,0 +1,42 @@
+import autofit as af
+import autolens as al
+
+
+def test_chained_mass_and_fields_keep_external_shear_in_model_and_tracer():
+    stage_1 = af.Collection(
+        galaxies=af.Collection(
+            lens=af.Model(al.Galaxy, redshift=0.5, mass=af.Model(al.mp.Isothermal)),
+            source=af.Model(al.Galaxy, redshift=1.0, bulge=af.Model(al.lp.Sersic)),
+        ),
+        fields=af.Model(
+            al.MassField, redshift=0.5, shear=af.Model(al.mp.ExternalShear)
+        ),
+    )
+
+    mass, field = al.util.chaining.mass_and_fields_from(
+        mass=af.Model(al.mp.PowerLaw),
+        mass_result=stage_1.galaxies.lens.mass,
+        fields_result=stage_1.fields,
+    )
+    stage_2 = af.Collection(
+        galaxies=af.Collection(
+            lens=af.Model(al.Galaxy, redshift=0.5, mass=mass),
+            source=stage_1.galaxies.source,
+        ),
+        fields=field,
+    )
+
+    assert ("fields", "shear", "gamma_1") in stage_2.unique_prior_paths
+
+    instance = stage_2.instance_from_prior_medians()
+    tracer = al.Tracer(galaxies=instance.galaxies, fields=instance.fields)
+    assert len(tracer.fields) == 1
+    assert isinstance(tracer.fields[0], al.MassField)
+
+    _, fixed_field = al.util.chaining.mass_and_fields_from(
+        mass=af.Model(al.mp.PowerLaw),
+        mass_result=stage_1.galaxies.lens.mass,
+        fields_result=stage_1.instance_from_prior_medians().fields,
+    )
+    fixed_stage = af.Collection(galaxies=stage_2.galaxies, fields=fixed_field)
+    assert ("fields", "shear", "gamma_1") not in fixed_stage.unique_prior_paths