[Jammy2211]

Summary

Adds a JAX-native interpolator that approximates InterpolatorDelaunay
without the scipy.spatial.Delaunay callback. For each query point it
picks the 3 nearest mesh vertices via the existing brute-force kNN
search and computes locally-exact barycentric weights on the triangle
they form. When the 3 nearest happen to be the containing Delaunay
triangle's vertices, weights are bit-identical to Delaunay; otherwise
weights are clipped non-negative and renormalized. Degenerate
(collinear) triangles fall back to nearest-neighbor weight [1, 0, 0],
matching pix_indexes_for_sub_slim_index_delaunay_from's outside-simplex
policy.

This is the library half of the wildcard speedup investigated in issue
#317 — the scipy callback costs ~16.87 ms/element (24% of the production
batched likelihood at batch=20 on A100). Scientific validation against
real lens models at rtol=1e-3 on log_evidence happens in the
follow-up workspace PRs (autolens_workspace_developer regression
script + autolens_workspace_test smoke).

API Changes

Purely additive:

• New aa.mesh.KNNBarycentric(pixels=...) mesh class, inherits from
  KNearestNeighbor (so all the existing kNN regularization-spacing
  knobs work) but selects the new interpolator.
• New InterpolatorKNNBarycentric (not exported at aa. top level,
  same as InterpolatorKNearestNeighbor).
• New barycentric_weights_from_3_nearest() helper in
  autoarray.inversion.mesh.interpolator.knn.

No removals, no signature changes, no behaviour changes to existing
mesh/interpolator classes. See full details below.

Test Plan

• pytest test_autoarray/inversion/pixelization/interpolator/test_knn_barycentric.py — 7/7 pass
• pytest test_autoarray/inversion/pixelization/interpolator/ test_autoarray/inversion/pixelization/mesh/ — 24/24 pass, no regression
• Full test_autoarray/ suite — run by ship_library subagent
• Scientific validation (rtol=1e-3 vs Delaunay log_evidence at HST fiducial 26288.321397232066) — follow-up workspace PR

Full API Changes (for automation & release notes)

Added

• autoarray.inversion.mesh.mesh.knn.KNNBarycentric(pixels, zeroed_pixels=0, k_neighbors=10, radius_scale=1.5, areas_factor=0.5, split_neighbor_division=2) — mesh class. Inherits all knobs from KNearestNeighbor; only interpolator_cls differs. Available as aa.mesh.KNNBarycentric.
• autoarray.inversion.mesh.interpolator.knn.InterpolatorKNNBarycentric — interpolator class. Subclass of InterpolatorKNearestNeighbor; overrides _mappings_sizes_weights and _mappings_sizes_weights_split to call kNN with k=3 and use barycentric instead of Wendland weights.
• autoarray.inversion.mesh.interpolator.knn.barycentric_weights_from_3_nearest(query_points, mesh_points, nearest_3_indices, xp) — helper that computes signed barycentric weights on the triangle formed by the 3 nearest mesh vertices, clips non-negative, renormalizes, and falls back to nearest-neighbor on degenerate triangles.

Removed / Renamed / Signature Changed

• None.

Behaviour Changed

• None for existing classes. KNearestNeighbor and InterpolatorKNearestNeighbor are untouched.

Migration

• No migration required. Users opt in by swapping aa.mesh.Delaunay(pixels=...) (or aa.mesh.KNearestNeighbor(pixels=...)) for aa.mesh.KNNBarycentric(pixels=...).

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