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>

giant/sample.py

@@ -43,10 +43,15 @@ def sample_secondaries(
 
     n_sec_pred: (B,) int64 — number of valid secondaries per step
 
-    Returns (sec_cont, sec_type_emb, sec_valid):
-        sec_cont:     (B, K_MAX, 4) — [stick_logit, local_dir_x, local_dir_y, local_dir_z]
-        sec_type_emb: (B, K_MAX, emb_dim) — predicted type embedding per slot
-        sec_valid:    (B, K_MAX) bool — True for slots i < n_sec_pred
+    Returns (sec_cont, sec_phys, sec_valid):
+        sec_cont:  (B, K_MAX, 4) — [stick_logit, local_dir_x, local_dir_y, local_dir_z]
+        sec_phys:  (B, K_MAX, PARTICLE_PHYS_DIM) — predicted [log_mass, charge]
+                   per slot (normalised iff the checkpoint's sec_phys
+                   normalizer was applied at training time — denormalize
+                   before treating as physical units; see
+                   giant.data.transforms.decode_secondaries). Used as-is —
+                   no snapping to a discrete PDG code.
+        sec_valid: (B, K_MAX) bool — True for slots i < n_sec_pred
     """
     sec_decoder.eval()
     B = cond_cont.size(0)
@@ -61,27 +66,12 @@ def sample_secondaries(
 
     x_slots = x.view(B, K_MAX, SEC_SLOT_DIM)
     sec_cont = x_slots[:, :, :4]
-    sec_type_emb = x_slots[:, :, 4:]
+    sec_phys = x_slots[:, :, 4:]
 
     sec_valid = torch.arange(K_MAX, device=device).unsqueeze(0) < n_sec_pred.unsqueeze(
         1
     )
-    return sec_cont, sec_type_emb, sec_valid
-
-
-def snap_type_to_pdg_idx(
-    sec_type_emb: torch.Tensor,
-    pdg_emb_weight: torch.Tensor,
-) -> torch.Tensor:
-    """Nearest-neighbour snap: predicted type embedding → PDG model-index.
-
-    sec_type_emb: (B, K_MAX, emb_dim)
-    Returns (B, K_MAX) int64 with model-indices.
-    """
-    B, K, D = sec_type_emb.shape
-    flat = sec_type_emb.reshape(-1, D)
-    dists = torch.cdist(flat.float(), pdg_emb_weight.float())
-    return dists.argmin(dim=-1).reshape(B, K)
+    return sec_cont, sec_phys, sec_valid
 
 
 @torch.no_grad()