Condition on material/particle physical properties instead of learned embeddings

Adds model.conditioning = "physical" | "embedding": physical mode routes
particle mass/charge and material Z_eff/A_eff/density/X0/lambda_int through
small MLPs to replace the learned PDG/material embedding tables, so the
surrogate generalizes to PDG codes/materials outside the training vocab
instead of memorizing it. "embedding" stays available as the comparison
baseline (old checkpoints without the key default to it).

Stage 2 now regresses a secondary's mass/charge directly against a fixed
physics-derived target instead of a learned/snapped embedding, and uses no
snapping at inference — the model's raw predicted (mass, charge) is the
secondary's physical identity, including for its own further rollout steps.
A separate reporting-only nearest-known-PDG lookup (never fed back into the
model) populates output pdg columns / the embedding-mode rollout fallback.

giant/materials.py's table is populated with Geant4's own built-in NIST
constants (Z_eff, A_eff, density, X0, lambda_int), extracted directly from
the Geant4 11.4.1 build vendored in minicalosim via G4NistManager rather
than hand-typed literature values. G4_LYSO is left unfilled: confirmed (both
by runtime lookup and by searching minicalosim's history) that it's never
actually a constructed Geant4 material there, only documentation/UI color-map
text.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_router.py

@@ -576,11 +576,9 @@ def test_routed_denoising_mlp_predict_n_sec_shape():
     assert logits.shape == (B, K_MAX + 1)
 
 
-def test_routed_denoising_mlp_pdg_embedding_weight_shape():
+def test_routed_denoising_mlp_has_no_pdg_embedding_weight_method():
     model = _routed_stage1(pdg=5, mat=2)
-    from giant.constants import EMB_DIM
-
-    assert model.pdg_embedding_weight().shape == (5, EMB_DIM)
+    assert not hasattr(model, "pdg_embedding_weight")
 
 
 # ── RoutedSecondaryDecoder ───────────────────────────────────────────────────