Add post_pos as a model target via travel_dir decomposition

step_length already encodes |post_pos - pre_pos| by definition, so a raw
post_pos target would duplicate that magnitude and could drift inconsistent
with step_length during sampling. Instead add travel_dir, a unit vector
(local frame) giving only the direction of pre_pos->post_pos; post_pos is
reconstructed at inference as pre_pos + step_length * travel_dir, keeping
the two self-consistent. Target grows from 6D to 9D.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

giant/data/loader.py

@@ -30,6 +30,7 @@ def _df_to_dict(df: pd.DataFrame) -> dict[str, np.ndarray]:
         "delta_e": (df["pre_E"] - df["post_E"]).to_numpy(dtype=np.float32),
         "edep": df["edep"].to_numpy(dtype=np.float32),
         "post_dir": df[["post_dx", "post_dy", "post_dz"]].to_numpy(dtype=np.float32),
+        "post_pos": df[["post_x", "post_y", "post_z"]].to_numpy(dtype=np.float32),
     }