prompt: close numerical-inversion-failures (refuted); ship + scope the autoarray noise-map cluster

complete/2026/08/numerical-inversion-failures.md

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+# numerical-inversion-failures — release-run non-positive-definite inversion failures
+
+**Date:** 2026-08-22
+**Issue:** [PyAutoArray#467](https://github.com/PyAutoLabs/PyAutoArray/issues/467) (closed)
+**PRs:** none — **no code was changed in any repo**
+**Outcome:** investigated to a definitive verdict; no defect exists to fix.
+
+## What this task was
+
+Release run `28784914443` (PyAutoHeart#27, the first real release-profile validation) reported 42
+script failures, split across seven prompts in `PyAutoMind/draft/bug/health_fixes/`. This task owned
+two, both alleging `LinAlgError`-class failures from non-positive-definite matrices in inversion
+paths:
+
+- `autolens_workspace_test/scripts/interferometer/model_fit.py`
+- `autogalaxy_workspace/scripts/interferometer/features/pixelization/galaxy_reconstruction.py`
+
+The prompt asserted the autolens leg "reproduces on current `main`" and prescribed a five-step
+repair: capture curvature/regularization matrix properties at failure, localise the defect between
+sampled parameters, regularization construction, numerical stabilization or an underdetermined
+script model, then fix the owning library.
+
+Brain sized it `too-large` (score 16), fix-locus "library source", strategy "split into phases".
+
+## What was actually done
+
+Not started as the prescribed repair. The premise was gated first, for the same reason its sibling
+`autofit_sampler_database` was: the claim was six weeks old, and the cluster around it had already
+produced two independent findings that it had aged out.
+
+Both scripts were re-run on current `main` from a **cleared** `output/`, under each workspace's
+`config/build/profile_release.yaml`, env resolved by `autohands.env_config.build_env_for_script` at
+workspace CWD, 1800s `mode=release` cap. Libraries at `main`: PyAutoFit `248ca971f`, PyAutoArray
+`b808a9b1`, PyAutoGalaxy `7e3856dd`, PyAutoLens `d8f6bb3df`, PyAutoNerves `f6d6d52`. Three workspace
+checkouts were behind `origin/main` and were synced first.
+
+## Result: 0 / 2 reproduce
+
+| Script | Result | Secs |
+|---|---|--:|
+| `autolens_workspace_test/scripts/interferometer/model_fit.py` | PASS | 78 |
+| `autogalaxy_workspace/scripts/interferometer/features/pixelization/galaxy_reconstruction.py` | PASS | 70 |
+
+No non-positive-definite failure and no `LinAlgError` in either. The prompt's specific claim that the
+autolens leg reproduces on `main` is false.
+
+## Why this verdict is strong: two prior independent refutations of the same hypothesis
+
+This is not a lone green run. The same hypothesis — that PyAuto's inversion path produces
+non-positive-definite curvature/regularization matrices — had already been tested and refuted twice:
+
+1. **`complete/2026/07/pix-inversion-not-positive-definite.md`** (2026-07-21) investigated a
+   six-marker `LinAlgError: matrix not positive definite / singular` cluster across
+   autogalaxy_workspace + HowToGalaxy. Outcome **inverted**: all six markers were stale and **no code
+   was fixed**. The pix `LinAlgError` had been cured on 2026-04-10 — the same day the markers were
+   filed — by PyAutoArray's `GaussianKernel` PD-guarantee `f1817af0` (symmetrise + trace-scaled
+   diagonal jitter). Evidence was a 40-draw numpy inversion A/B across the full `GaussianKernel`
+   LogUniform prior (coeff/scale `1e-6`..`~5e5`): **0 raises / 0 non-finite** on both `cholesky` and
+   `slogdet`.
+2. **`complete/2026/08/autofit-sampler-database.md`** (PyAutoFit#1508, 2026-08-21) — the sibling from
+   this same release run, **0/9 reproduce**, closed with no code change.
+
+A PD-guarantee landed in the owning library four months ago, was independently verified by direct
+matrix probing, and both of this prompt's scripts now pass. The defect described here does not exist
+on `main`.
+
+## Why the issue was closed rather than parked open
+
+The same test the sibling applied, and it passes here: **neither script is parked.** Checked against
+`main` on 2026-08-22 —
+
+- `autogalaxy_workspace/config/build/no_run.yaml` — 8 entries, none matching
+  `interferometer/features/pixelization/galaxy_reconstruction` (GUI scripts, fits/png_make,
+  search-viz, and one SLOW shapelets entry).
+- `autolens_workspace_test/config/build/no_run.yaml` — no `interferometer/model_fit` entry. It
+  appears only as a *consumer* on line 62, where `interferometer/simulator/with_lens_light.py` is
+  marked `BOOTSTRAP-TARGET` because it produces `model_fit`'s dataset — which confirms `model_fit`
+  itself runs.
+
+So both scripts re-execute under exactly this profile in **every** `mode=release` pass. Re-validation
+is automatic; a surviving defect fails the next release run loudly and earns a fresh issue with fresh
+evidence. There is no human reminder to lose — the same reasoning that closed PyAutoFit#1508, and the
+reason this prompt closes while `samples_parameter_paths` (#1327) stays parked.
+
+This is now the **fourth** independent finding that this cluster aged out. The consistent explanation
+across all of them is the one #1327 reached: stale cached `output/` in the 2026-07 release run,
+against libraries that have since absorbed dozens of fixes.
+
+## What this does NOT establish
+
+1. **The autolens leg ran on numpy, not JAX.** `autolens_workspace_test`'s release profile *defaults*
+   `PYAUTO_DISABLE_JAX="1"`; scripts opt back in with an in-file `ENV: jax` declaration, and
+   `model_fit.py` carries none. That is release-faithful — it is what the release run itself executes
+   — but JAX-on and JAX-off are different numerical code paths, and this refutation covers only the
+   numpy one. A JAX-only inversion conditioning defect would not appear here.
+2. **These were source-tree runs**, not the TestPyPI wheels the release run installed. A wheel-only
+   packaging defect would not show.
+
+## Incidental finding — filed as its own prompt
+
+`galaxy_reconstruction.py` passes while emitting 4x `RuntimeWarning: invalid value encountered in
+sqrt` from `PyAutoArray/autoarray/inversion/inversion/abstract.py:859`. **This looks exactly like
+evidence for the prompt's hypothesis and is not** — it is a separate, unconditional defect:
+
+```python
+@property
+def reconstruction_noise_map_with_covariance(self) -> np.ndarray:
+    return np.sqrt(np.linalg.inv(self.curvature_reg_matrix))
+```
+
+`sqrt` is applied **elementwise to the whole inverse matrix**, whose off-diagonal entries are
+covariances and are generally negative — so those entries are NaN *by construction*, for any input
+matrix, however well-conditioned. It is not a conditioning symptom and does not rescue the prompt.
+
+Confirmed still present on PyAutoArray `main` @ `a6b07cd` (2026-08-22). The 1D
+`reconstruction_noise_map` is unaffected — it takes the diagonal, and elementwise-sqrt commutes with
+taking the diagonal — so the science path is correct; only the covariance-aware consumer and the
+warning spam are hit.
+
+Filed as **`draft/bug/autoarray/reconstruction_noise_map_covariance_sqrt.md`**, not fixed here: the
+correct off-diagonal semantics are an API/science decision, and the fix has a real trap (the 1D
+science path is derived from this property's diagonal, so a naive change silently converts a
+standard-deviation into a variance).
+
+
+**2026-08-22 follow-up — the incidental finding got bigger.** Research into *why* source-reconstruction
+noise maps have been unreliable found the sqrt bug is **not** the cause: `np.sqrt` is elementwise, so it
+commutes with taking the diagonal and provably cannot reach the 1D noise map. Three deeper defects were
+found in the same property, and split into a second prompt,
+`draft/bug/autoarray/reconstruction_noise_map_solver_mismatch.md`:
+
+1. **Estimator mismatch (the big one).** `inv(curvature_reg_matrix)` is the posterior covariance of the
+   *unconstrained* Warren & Dye solve. But `use_positive_only_solver: true` is the shipped default, so the
+   reconstruction is an NNLS active-set solve. Imposing `s >= 0` truncates the posterior, so the reported
+   noise is overstated near the boundary and meaningless for pinned pixels — and a compact lensed source
+   pins a large fraction of the mesh at zero, so the formula is worst exactly where it is most used.
+2. **Edge-zeroed pixels ignored.** `use_edge_zeroed_pixels: true` is also default; the reconstruction
+   solves on `zeroed_ids_to_keep` and scatters back zeros, while the noise map inverts the *full* matrix —
+   re-admitting the poorly-constrained boundary vertices the zeroing exists to remove.
+3. **`use_edge_zeroed_pixels` is nested inside the positive-only branch**, so turning the positive-only
+   solver off silently disables edge-zeroing with no warning. Two orthogonal settings, silently coupled.
+
+Corroboration for the numerics half: `abstract.py:805` already documents `~1e-6` evidence round-off from
+"factorizing the explicitly formed inverse" at `cond(C) ~ 1e9` on clustered traced mesh vertices — applied
+to the log-det, never to the noise map. And `inversion_plots.py:395` already wraps the noise map in
+`except np.linalg.LinAlgError`, writing NaN to the CSV: a guard that exists because this fails in practice.
+
+## Follow-on state of the cluster
+
+`draft/bug/health_fixes/README.md` row struck through. Of the original seven prompts: two shipped or
+closed with code (`aggregator_output_contracts`), two closed as refuted with no code change
+(`autofit_sampler_database`, this one), one parked not-reproducing (`samples_parameter_paths`,
+#1327), and three remain in `draft/` with dated gate annotations
+(`jax_runtime_and_parity`, `jit_visualization_outputs`, `release_timeout_policy`) — those are *not*
+complete, because their SLOW/NEEDS_FIX parkings describe *intermittent* failures that a single green
+run cannot clear.
+
+## Original prompt
+
+# Fix release-profile numerical inversion failures
+
+Type: bug
+Target: health_fixes
+Difficulty: too-large
+Autonomy: supervised
+Priority: high
+Status: formalised
+
+## Context
+
+Two interferometer scripts fail in inversion paths with non-positive-definite matrices.
+The Autolens test failure reproduces on current `main`; the Autogalaxy script passed in a
+stateful local checkout and needs a clean confirmation.
+
+Owners: @PyAutoArray, @PyAutoGalaxy, @PyAutoLens, @autogalaxy_workspace, and
+@autolens_workspace_test.
+
+## Scripts
+
+- `autogalaxy_workspace/scripts/interferometer/features/pixelization/galaxy_reconstruction.py`
+- `autolens_workspace_test/scripts/interferometer/model_fit.py`
+
+## Required work
+
+1. Reproduce in clean output/worktrees with deterministic seeds and release settings.
+2. Capture the curvature and regularization matrix properties at failure: symmetry,
+   conditioning, eigenvalue range, dtype, backend, and mapper configuration.
+3. Identify whether the defect is invalid sampled parameters, regularization construction,
+   numerical stabilization, or a script model that permits an undefined inversion.
+4. Fix the owning library for valid inputs. Do not catch `LinAlgError` or alter the script
+   to hide a genuine inversion failure.
+5. Add numerical regression tests and rerun both scripts repeatedly under the profile.
+
+<!-- formalised retroactively by the Intake (Conception) Agent on 2026-07-08 -->
+
+## 2026-08-21 — REPRODUCTION GATE RUN: **2/2 PASS — prompt refuted**
+
+Method (identical to the gate that closed the sibling `autofit_sampler_database`, PyAutoFit#1508):
+every script run from a **cleared** `output/`, under its workspace's
+`config/build/profile_release.yaml`, env resolved by `autohands.env_config.build_env_for_script`
+at workspace CWD, 1800s `mode=release` cap. Libraries at `main`: PyAutoFit `248ca971f`,
+PyAutoArray `b808a9b1`, PyAutoGalaxy `7e3856dd`, PyAutoLens `d8f6bb3df`, PyAutoNerves `f6d6d52`.
+Three workspace checkouts were **behind `origin/main`** and were synced first.
+
+| Script | Result | Secs |
+|---|---|--:|
+| `autolens_workspace_test/scripts/interferometer/model_fit.py` | PASS | 78 |
+| `autogalaxy_workspace/scripts/interferometer/features/pixelization/galaxy_reconstruction.py` | PASS | 70 |
+
+The prompt states the autolens leg "reproduces on current `main`". It does not. No
+non-positive-definite failure, no `LinAlgError`, in either.
+
+**Note which numerical path each took.** `autolens_workspace_test`'s release profile *defaults*
+`PYAUTO_DISABLE_JAX="1"`, and scripts opt back in with an in-file `ENV: jax` declaration.
+`model_fit.py` has no such declaration, so it ran on **numpy** — release-faithful, but worth
+knowing for a claim about inversion numerics, since JAX-on and JAX-off are different code paths.
+
+### Incidental finding — a real defect, but NOT this prompt's
+
+`galaxy_reconstruction.py` passes while emitting 4x
+`RuntimeWarning: invalid value encountered in sqrt` from
+`PyAutoArray/autoarray/inversion/inversion/abstract.py:859`:
+
+```python
+def reconstruction_noise_map_with_covariance(self):
+    return np.sqrt(np.linalg.inv(self.curvature_reg_matrix))
+```
+
+`sqrt` is applied **elementwise to the whole inverse matrix**, whose off-diagonal entries are
+covariances and are generally negative — so those entries are NaN *by construction*, for any
+matrix, however well-conditioned.
+
+**This is not evidence of a non-positive-definite matrix** and does not rescue the prompt's
+hypothesis, despite looking exactly like it would. It is a separate defect: a property whose
+docstring promises a matrix that "accounts for the covariance of the noise between pixels" returns
+NaN wherever that covariance is negative. The 1D `reconstruction_noise_map` is unaffected — it
+takes the diagonal, and `diag(sqrt(M)) == sqrt(diag(M))` — so the science path is correct; only
+the covariance-aware consumer and the warning spam are hit. Worth its own PyAutoArray prompt.