Activity

1. Jammy2211 commented on Jul 27, 2026

   

   Jammy2211

   on Jul 27, 2026

   CollaboratorAuthor

   More actions

   Phase 1 design — point-source likelihood variants (Lombardi 2024, arXiv:2406.15280)

   All code facts below verified against main on 2026-07-27.

   D0. Paper results this design builds on

   • Linearized source-plane likelihood (§5.1): Taylor-expand the lens equation around each observed image θ̂ᵢ, giving β ≈ β̂ᵢ + Aᵢ(θ̂ᵢ − θᵢ) with Aᵢ the lens Jacobian at the observed position. β then enters the chi-squared linearly and is solved in closed form:
     β* = (Σᵢ AᵢᵀΘᵢAᵢ)⁻¹ Σᵢ AᵢᵀΘᵢ β̂ᵢ (Θᵢ = image-plane position precision).
     Marginalizing β (flat prior) adds a −½ log|Σᵢ AᵢᵀΘᵢAᵢ|-type determinant term. The per-image precision transform AᵢᵀΘᵢAᵢ is the tensor weighting — the principled version of our scalar µ² weighting, and the "better errors" variant.
   • Missing-image penalty (Eq. 32): the all-pairings marginalization normalizes by 1/|Σ|, |Σ| = P^I with repetition — a purely combinatorial Occam penalty. Verified: FitPositionsImagePairAll.chi_squared implements exactly this (n_permutations = n_finite_model^len(data), pair_all.py:145-154). Nothing new to implement; the without-repetition form (P!/(P−I)!, Eq. 37) requires factorial permutation enumeration — JAX-hostile, and rejected below.
   • Analytic nuisance closed forms (§6.1): M* = Σλᵢ(m̂ᵢ+LMᵢ)/Σλᵢ (magnitude space); T* = Στᵢ(t̂ᵢ−Tᵢ)/Στᵢ.
   • Honesty note: the paper never substitutes β* into its image-plane likelihoods (β stays free there). The centre-free image-plane variants below are our extension — a profile-likelihood hybrid with direct precedent in glafic's source-position-optimized image-plane χ². Docs must attribute accordingly.

   D1. Variant set (decision)

   #	Variant	Basis	Verdict
   S1	Source-plane, solved centre, tensor weighting + det term	paper §5.1	IMPLEMENT — becomes the recommended source-plane fit (cluster default candidate)
   S1b	S1 with scalar µ weighting	—	class-attribute option on S1 (weighting = "jacobian" | "magnification") for Lenstool-style comparisons; cheap, no new class
   S2	Existing FitPositionsSource (free centre, scalar µ²)	—	unchanged, kept for continuity
   I1	All-to-all image-plane, solved centre	our extension (glafic precedent)	IMPLEMENT
   I2	Pair-repeat image-plane, solved centre	our extension	IMPLEMENT
   I3	Hungarian FitPositionsImagePair, solved centre	—	SKIP — numpy-only (scipy Hungarian, not jittable), superseded by I1/I2; documented why
   P	Missing-image penalty	paper Eq. 32	verification + docs only — already correct in PairAll; PairRepeat's unmatched_model_policy heuristics stay as-is (best-match is not a normalized mixture; no principled combinatorial term applies)
   F	Analytic flux F*	paper §6.1, ported	IMPLEMENT, flux space: F* = Σᵢ µᵢ f̂ᵢ/σᵢ² / Σᵢ µᵢ²/σᵢ² + −½log(Σµᵢ²/σᵢ²) marginalization term. Magnitude space (paper-exact) REJECTED: our noise maps are flux-space Gaussians; converting changes the error model, not just the parametrization. Magnification-first, matching `FitFluxes.model_data =
   T	Analytic reference time T*	paper §6.1	IMPLEMENT: precision-weighted T* + norm term, as alternative to the current double min-subtraction (times_delays.py:109-110)

   D2. API (decision)

   Model class — one new, parameter-free class in autogalaxy/profiles/point_sources.py:

   class PointSolved:   # al.ps.PointSolved — zero non-linear parameters
       ...

   • Removing the (y,x) priors requires this: priors derive from __init__, so no fit_positions_cls setting can do it.
   • No flux sibling needed: the analytic-flux fit never reads profile.flux (F* comes from data + magnifications), so PointSolved covers positions+fluxes+delays datasets alone. FitFluxes (free flux) keeps requiring PointFlux.
   • Name pairing (fit/abstract.py:84-90) unchanged — pairing is by dataset/profile name, not class.

   Fit side — a SolvedCentre mixin overriding the single funnel:

   • All fits already resolve the model centre through one property: AbstractFitPoint.source_plane_coordinate → profile.centre (abstract.py:144-153). The mixin overrides that property to return β* (tensor-weighted back-projection); everything downstream (incl. the PointSolver forward solve for image-plane fits, image/abstract.py:121-127) is untouched.
   • Concrete classes: FitPositionsSourceSolved, FitPositionsImagePairAllSolved, FitPositionsImagePairRepeatSolved, FitFluxesSolved, FitTimeDelaysSolved (names open to bikeshed).
   • Loud mismatches, both directions (no silent laundering): a centre-requiring fit given PointSolved raises with a message naming the Solved variant; a Solved fit given Point/PointFlux raises (otherwise the centre priors would be sampled but ignored — silent 2-parameter waste).
   • New hooks: fit_flux_cls / fit_time_delays_cls on FitPointDataset and AnalysisPoint, mirroring fit_positions_cls; defaults = current FitFluxes/FitTimeDelays (behaviour unchanged). Currently hard-wired at fit/dataset.py:114,130.
   • The five false "barycenter" docstrings (positions/abstract.py:48, image/abstract.py:49, pair.py:30, pair_all.py:26, pair_repeat.py:18) become true by pointing at the mixin; the workspace guide's false profile=None claim (cluster/likelihood_function.py:312-318) is fixed in phase 4.

   Jacobian source: A = I − ∂α/∂θ assembles from ag.LensCalc.hessian_from (lens_calc.py:382, xp-threaded, JAX path exists) — same machinery as today's Hessian magnifications; no new differential-geometry code.

   D3. JAX plan (decision)

   • β*, F*, T* are fixed-shape linear algebra (2×2 solve / scalar ratios) — xp-threaded from day one; new fit classes registered in AnalysisPoint._register_fit_point_pytrees (model/analysis.py:181-218).
   • In scope for phase 2 (approved rationale: the new source-plane variant sits on the same primitives): fix the two known FitPositionsSource jit blockers — Grid2DIrregular.grid_2d_via_deflection_grid_from gets xp (PyAutoArray/autoarray/structures/grids/irregular_2d.py:170), and FitPositionsSource joins the pytree registration. This makes the series a 3-repo task: PyAutoArray + PyAutoGalaxy + PyAutoLens (recorded override of the FeatureDecision's single-repo estimate).
   • Solver gradients (custom_jvp via the fixed-point rule ∂θᵢ/∂p = Aᵢ⁻¹ ∂α/∂p) remain phase 5; phase 3's gradient scripts cover only the solver-free solved variants.

   D4. Phase-2 test matrix (numpy-only; JAX via workspace_test scripts only)

   1. β* equals brute-force minimizer of the tensor source-plane χ² over centre (fixed mock tracer).
   2. Solved-centre log-likelihood ≈ free-centre likelihood profiled over centre, per scheme (S1, I1, I2).
   3. Tensor vs scalar: an anisotropic-magnification mock where tensor weighting reproduces the image-plane χ² ordering scalar µ² gets wrong.
   4. F*/T* equal brute-force scans; solved ≈ profiled parity for both.
   5. Penalty regression: n_permutations == P_finite^I on a counted mock; predicted-image surplus lowers likelihood monotonically.
   6. Loud-failure matrix: {Point,PointFlux,PointSolved} × {centre-requiring, solved} fits — every invalid cell raises with an actionable message.
   7. Structure/__Env__ conventions mirror test_autolens/point/.

   D5. Consequences for phases 3–5

   • Phase 2 scope grows by: fit_flux_cls/fit_time_delays_cls hooks, FitFluxesSolved, FitTimeDelaysSolved, the PyAutoArray xp fix. Phase prompts updated accordingly.
   • Recommended-default question (does S1 become the documented cluster default over FitPositionsSource?) is answered by phase 3's cluster likelihood_breakdown numbers + phase 2's error-fidelity tests; phase 4 writes the recommendation.
2. Jammy2211 commented on Jul 27, 2026

   

   Jammy2211

   on Jul 27, 2026

   CollaboratorAuthor

   More actions

   Convention correction to D0/D2 above (before phase 2 reads it): with the standard lensing convention A ≡ ∂β/∂θ (so A⁻¹ is the magnification tensor, µ = 1/det A), the per-image precision on a back-traced position is Wᵢ = Aᵢ⁻ᵀ Θᵢ Aᵢ⁻¹ — inverse Jacobians, i.e. magnification-tensor weighting — and β* = (Σᵢ Wᵢ)⁻¹ Σᵢ Wᵢ β̂ᵢ. Sanity check: the isotropic limit gives Wᵢ → µᵢ²/σᵢ², matching the existing scalar weighting in FitPositionsSource.chi_squared_map. The AᵀΘA form written above is correct only if A is read as ∂θ/∂β; taken in the standard convention it would deweight high-magnification images — backwards. Phase 2 implements Wᵢ = Aᵢ⁻ᵀΘᵢAᵢ⁻¹ with A assembled from LensCalc.hessian_from, and the parity/brute-force tests in D4 guard the sign of this choice.
3. Jammy2211 commented on Jul 27, 2026

   

   Jammy2211

   on Jul 27, 2026

   CollaboratorAuthor

   More actions

   Resolution of the isotropic-limit dispute (raised by the phase-2 implementer — they were right): with A = aI, µ = 1/a², so the tensor weighting's isotropic limit is W → (µ/σ²)I, not µ²/σ² as my correction comment claimed. Full picture: the eigenvalues of W = σ⁻²A⁻ᵀA⁻¹ are λ²/σ² with λ the linear stretch per eigendirection — isotropically each stretch is õ (→ µ/σ²), while near a critical curve the tangential stretch is ≈ µ (→ µ²/σ² along the arc). So the existing scalar µ²/σ² convention (FitPositionsSource, Lenstool-style) is the near-critical tangential limit, not the isotropic one, and the tensor is exact in both regimes. Implementation keeps both: weighting=\"jacobian\" (exact, default) and weighting=\"magnification\" (traditional scalar, for comparisons). Also fixed in review: the 2-D marginalization constant is now the exact (2π)² Gaussian-integral normalization. Bonus catch by the implementer: ag.LensCalc.jacobian_from returns (x,y)-ordered components against the codebase's (y,x) convention — invisible to its determinant-only existing callers, re-indexed at the new consumption site and flagged in its docstring.
4. Jammy2211 commented on Jul 27, 2026

   

   Jammy2211

   on Jul 27, 2026

   CollaboratorAuthor

   More actions

   Phase 2 library PRs created (all suites green: autoarray 906, autogalaxy 1009, autolens 473; all labeled pending-release):

   • feat: thread xp through Grid2DIrregular.grid_2d_via_deflection_grid_from PyAutoArray#414 — xp threading for grid_2d_via_deflection_grid_from (merge first)
   • feat: add zero-parameter ps.PointSolved point-source profile PyAutoGalaxy#531 — ag.ps.PointSolved (+ jacobian_from ordering docstring warning)
   • feat: analytically-solved point-source likelihood variants (#657) #659 — the five *Solved fit classes, SolvedCentre mixin, fit_flux_cls/fit_time_delays_cls hooks, pytree registrations, mismatch errors, docstring truth sweep

   API changes are additions-only — no existing script is affected. Heart was YELLOW at ship (pre-existing, unrelated reasons; human-acknowledged: workspace validation 13-failed of 2026-07-21 [predates today's searches-shard fix], 33 stale parked scripts, tenant-firewall manifest drift ×5).

   Next: phases 3 (workspace_test jax_likelihood + profiling likelihood_runtime/breakdown examples) and 4 (guides) via /start_workspace after these merge; phase 5 (JAX solver gradients) last.
5. Jammy2211 commented on Jul 27, 2026

   

   Jammy2211

   on Jul 27, 2026

   CollaboratorAuthor

   More actions

   Independent Codex review (codex-cli 0.145.0) of the three PRs: FINDINGS — 9 items. Disposition after adversarial verification:

   #	Finding	Verdict	Action
   1	noise_normalization used det Wᵢ (model-dependent) instead of observed-plane det Θᵢ — spuriously favours high magnification	CONFIRMED	Fixed: normalization is now Σ log((2π)²σᵢ⁴); FitPositionsSource's magnified-noise convention intentionally unchanged, difference documented
   2	jacobian_from crashes under jit for all solved fits	REFUTED empirically — solved source-plane runs under vmap+jit with exact numpy parity (−94.70750993 both backends)	none
   3	xp.asarray(deflection_grid) hits __array__ on traced wrappers	CONFIRMED (reproduced: TracerArrayConversionError in the workspace_test harness)	Fixed: unwrap .array before subtraction; harness now passes where it crashed
   4	Mismatch exceptions swallowed by FitPointDataset's component-skip except blocks	CONFIRMED (worse than stated — all three components)	Fixed: new exc.PointProfileMismatchException, deliberately not a PointExtractionException subclass; regression test through FitPointDataset
   5/6	New kwargs inserted mid-signature break positional callers	CONFIRMED	Fixed: hooks moved to signature ends
   7	xp=np default replaced the pre-existing self._xp propagation	CONFIRMED	Fixed: xp=None → self._xp
   8	MockPointSolver ignores source_plane_coordinate → forwarding untested	CONFIRMED (β*-vs-truth was tested; forwarding was not)	Fixed: RecordingMockSolver test
   9	Flux/time marginalization constants unpinned	CONFIRMED	Fixed: closed-form pins

   Bonus from the fix cycle: the pre-existing source-plane jit blocker is now genuinely cleared — scripts/point_source/jax_likelihood/source_plane.py's vmap+jit path passes (was TracerArrayConversionError); only the separately-tracked fit_from-returns-PointSolver pytree gap remains (pre-existing, out of scope).

   All suites green after fixes: autolens 477, autoarray 906, autogalaxy 1009. Fix commits pushed to PyAutoLabs/PyAutoArray#414 and #659 (PyAutoGalaxy#531 unchanged by the review).
6. Jammy2211

   added a commit that references this issue on Jul 27, 2026

   Merge pull request #659 from PyAutoLabs/feature/point-source-chi-squa…

   ...

   4a112ea
7. Jammy2211 commented on Jul 27, 2026

   

   Jammy2211

   on Jul 27, 2026

   CollaboratorAuthor

   More actions

   Phases 1+2 complete — all three library PRs MERGED (2026-07-27, post codex-review fixes; CI green on 3.12+3.13 everywhere): PyAutoLabs/PyAutoArray#414 → PyAutoLabs/PyAutoGalaxy#531 → #659. Feature branches and the task worktree are cleaned up; active.md released the library-repo claims.

   Resuming here (phases 3–5): run /start_workspace on PyAutoMind/draft/feature/autolens/point_source_chi_squared_paper_variants_phase_3_workspace_examples.md (workspace_test jax_likelihood + profiling likelihood_runtime/breakdown examples — note the solved source-plane path already passes vmap+jit with exact numpy parity, so phase 3 mostly extends harnesses + records baselines), then phase 4 (guides incl. the likelihood_function.py false-claim fix), then phase 5 (PointSolver custom_jvp gradients). The phase prompts carry the binding design deltas + this thread has the weighting-convention resolution.
8. Jammy2211 commented on Jul 30, 2026

   

   Jammy2211

   on Jul 30, 2026

   CollaboratorAuthor

   More actions

   Phase 3 shipped — 2026-07-30

   PRs (all merged)

   • Solved point-source fit coverage: jax_likelihood variants, gradients, fluxes+time delays (#657 phase 3) autolens_workspace_test#237 — solved-fit jax_likelihood coverage, gradients, fluxes+time-delays dataset
   • Solved point-source runtime profiling + cluster breakdown solved steps (#657 phase 3) autolens_profiling#96 — solved runtime siblings + cluster breakdown solved steps
   • Correct stale source-plane xp-blocker claims in jax_profiling (#657) autolens_workspace_developer#121 — stale xp-blocker prose corrected

   Summary

   Every phase-2 solved fit class now has JAX regression coverage: vmap literals with exact numpy parity for FitPositionsSourceSolved (−94.70750993), FitPositionsImagePairAllSolved (−82.33883111), FitPositionsImagePairRepeatSolved (−89.71129442), and the fluxes+time-delays combo (−137.67455417); jit(fit_from) passes exactly for all image-plane solved variants; finite-difference gradient checks pass for the solved source-plane fit. Profiling: the analytic β* step costs ~timing-noise-level overhead per likelihood (cluster image-plane net +3% across systems, source-plane +9–30% on sub-0.1 s eager totals) while removing 2 free parameters per point source from the non-linear space.

   Session Notes

   • The old source-plane BLOCKER (xp bug) is confirmed gone; the sole remaining jit(fit_from) gap is the fit-return pytree issue (PointSolver leaf, output component [1][1]) — tracked in PyAutoPrompt/autolens/fit_point_pytree.md, affects old and solved fits alike, vmap/log_likelihood_function unaffected.
   • Eager-vs-vmap parity for time-delay fits requires identical cosmology on both sides (H0-sensitivity) — the new script documents and asserts this with an H0-matched comparison; positions-only scripts are H0-independent so their existing pattern stands.
   • al.output_to_json has nondeterministic JSON key order run-to-run (value-identical output; PYTHONHASHSEED-driven) — filed in Mind ideas for a serializer fix.
   • imaging/subhalo_recovery.py exceeds the 300 s smoke cap on the WSL dev laptop (solo, quiet machine) while green on main CI — margin worth watching after the parked-sweep re-measure.
   • Remaining phases: 4 (guides, incl. the false profile=None-centroid claim in cluster/likelihood_function.py), 5 (PointSolver custom_jvp gradients).
9. Jammy2211 commented on Jul 31, 2026

   

   Jammy2211

   on Jul 31, 2026

   CollaboratorAuthor

   More actions

   Phase 4 (guides) PR open — 2026-07-31

   PR

   • docs: point-source solved-variant guides (phase 4 of #657) autolens_workspace#425 (pending-release, awaiting human merge)

   Upstream

   Library API already on main from phase 2 (PyAutoArray#414 / PyAutoGalaxy#531 / PyAutoLens#659) — no library-first merge gate applies.

   Summary

   The workspace guides now document the full likelihood-option matrix (pairing scheme × free-vs-solved centre): the pairing guide carries the deep prose (solved-variant table, tensor weighting = "jacobian"|"magnification" conventions, solved fluxes/time delays, missing-image-penalty status, runnable solved-centre demo), point_source/fit.py gains a runnable __Solved Source Centre__ section plus the honest defaults story (FitPointDataset → FitPositionsImagePair vs AnalysisPoint → FitPositionsImagePairRepeat), and the false FitPositionsSource(profile=None) back-traced-barycenter claim in cluster/likelihood_function.py is replaced with the real centre-free option. The deferred cluster recommendation (phase-1 design item) is now written from the phase-3 numbers: search with FitPositionsSourceSolved, validate with the image-plane chi-squared; demonstrated defaults in the modeling scripts are unchanged.

   Session Notes

   • Ship ran under an explicitly human-acknowledged Heart RED: the nightly Release Integrate failure is interferometer/start_here.py OOM (~86 GB JAX allocation) in the release-wheels leg — unrelated to this docs-only change; filed as PyAutoMind/draft/bug/autolens/interferometer_release_leg_oom.md.
   • Validation: local smoke suite 26/26 passed; the four code-changed scripts executed end-to-end (solved centre recovers the true (0.07, 0.07); fluxes solved model prints N=5 as documented).
   • Attribution: image-plane solved variants cite the glafic precedent (Oguri 2010) and are not attributed to Lombardi 2024; the scalar µ² weighting is labelled as the Lenstool-style convention.
   • Assistant-wiki refresh (point_source concept + skill pages) filed in Mind ideas for maintainer-mode once released.
   • Remaining: phase 5 (PointSolver custom_jvp gradients) — note PyAutoLens is currently claimed by potential-correction-validation (fix: potential-correction end-to-end validation (parity + evidence sampling) #672); hand-check file overlap before starting.
10. Jammy2211 commented on Jul 31, 2026

    

    Jammy2211

    on Jul 31, 2026

    CollaboratorAuthor

    More actions

    Phase 5 (solver gradients) PRs open — 2026-07-31

    PRs (library-first; all pending-release, awaiting human merge)

    • feat: PointSolver implicit-diff gradients (phase 5 of #657) #677 — the implicit-diff custom_jvp (merge FIRST)
    • test: solver-gradient certification + staircase FD methodology (phase 5 of #657) autolens_workspace_test#240 — certification blocks + staircase FD methodology
    • docs: point-source gradient probe status flip (phase 5 of #657) autolens_workspace_developer#123 — gradient-probe status flip
    • feat: multi_start_prodigy point_source image_plane_solved cell (phase 5 of #657) autolens_profiling#98 — multi_start_prodigy image_plane_solved cell

    Summary

    PointSolver positions now carry gradients via an implicit fixed-point custom_jvp (autolens/point/solver/implicit_diff.py): A dθ = dα|_θ + dβ at the solved positions — the same mechanism gravity.jl itself uses (the paper never differentiates through its solver; its autodiff only verifies the analytic rule). FD-certified across the variant matrix: solved and sampled centres through the solver (≤2% vs the stair-noise floor, spike best-steps <0.4%), PairRepeat subgradients live, and nested autodiff supplies the flux/magnification gradients the paper declares analytically out of reach. af.MultiStartProdigy can now run on image-plane point-source likelihoods (first search-integration cell registered in the profiling sweep). All values are unchanged: numpy path byte-identical, test_autolens/ 492 passed, every jax_likelihood regression literal re-verified.

    Session Notes

    • The historical "not differentiable" verdict was about differentiating THROUGH the refinement — still true. The implicit rule differentiates AT the solution; same verdict-goes-stale shape as the Delaunay frozen-tables arc.
    • Solver staircase: the forward solve quantizes at pixel_scale_precision, so naive FD reads exactly zero against a correct nonzero AD gradient (the exact-solve envelope derivative — what a gradient search wants). Certification uses a fine-precision solver + per-parameter FD step sweep; methodology encoded in the cert script prose.
    • Compatibility gate: the rule engages only when the tracer flattens to JAX leaves (the af.Model/Fitness registered path); hand-built tracers keep the old zero-gradient forward — nothing existing changes behaviour.
    • Known limitation (follow-up filed in Mind): free cosmology parameters cannot cross the custom_jvp boundary (Tracer keeps cosmology as no_flatten aux → stale tracer). Harmless for 2-plane positions; multi-plane free-cosmology gradient fits need cosmology flattened into the pytree.
    • Also filed: FitPositionsImagePairAll mixture underflows to -inf at ≳38σ mismatch (not a stable logsumexp) — shrinks the gradient-search basin; stabilization follow-up in Mind ideas.
    • Ship ran under the same human-acked Heart RED as phase 4 (nightly interferometer release-leg OOM, unrelated; re-acked for this ship). Local wst smoke 20/21 — sole failure is the known imaging/subhalo_recovery.py WSL 300s-cap margin, green on CI.
    • This completes the implementation phases of the series; phases 1–5 all shipped or awaiting merge.
11. Jammy2211

    added a commit that references this issue on Jul 31, 2026

    Merge pull request #677 from PyAutoLabs/feature/point-solver-implicit…

    ...

    8b9e20b
12. Jammy2211 commented on Jul 31, 2026

    

    Jammy2211

    on Jul 31, 2026

    CollaboratorAuthor

    More actions

    Gradient-search benchmark — the gradients work in a real search (2026-07-31)

    PR: PyAutoLabs/autolens_profiling#99 (truth-anchored Prodigy-vs-Nautilus on the galaxy-scale point-source dataset; full table + six result JSONs inside).

    Headline: MultiStartProdigy on PointSolved + FitPositionsImagePairAllSolved converges to the truth through the lens-equation solver — er 1.574, centre (0.00, 0.01) vs truth (1.6, (0,0)), logL +2.37 vs truth anchor +7.74, in ~16 min with the default 64×300 budget. This is the implicit-diff custom_jvp doing real work end-to-end.

    Supporting findings:

    • Free-centre image-plane Prodigy needs 256 starts to find the same basin (64 misses it; polish plateaus) — consistent with the filed PairAll -inf-underflow plateau issue and the solved variant's dimensionality edge.
    • The scalar-µ² free-centre source-plane likelihood is spectacularly biased here: truth logL −33788 while both samplers find −110…−313 at wrong parameters. Objective, not optimizer (Prodigy actually out-optimizes Nautilus 8.7× faster on it) — direct evidence for the tensor-weighted solved recommendation.

    Follow-ups filed in Mind per human decisions: cluster examples move demonstrated default to PointSolved (galaxy-scale stays free-centre); cluster-scale gradient-search benchmark next after point-source wraps.
13. Jammy2211 commented on Aug 6, 2026

    

    Jammy2211

    on Aug 6, 2026

    CollaboratorAuthor

    More actions

    Closing as shipped: design fully implemented across all 5 phases — PyAutoArray#414, PyAutoGalaxy#531, PyAutoLens#659/#677, autolens_workspace#425 all merged (2026-08-06 tracker reconciliation audit).
14. Jammy2211

    closed this as completedon Aug 6, 2026