Add autoregressive shower rollout driver

Closes the loop from single-step prediction into full showers:

- giant/geometry.py + `dwarf build-geometry-oracle`: learn position ->
  (material, layer_id) from data (KNN/SVM) to supply the conditioning the
  surrogate does not predict; flag detector escape by NN distance.
- giant/rollout.py: breadth-first batched frontier that steps all active
  tracks, spawns secondaries as new tracks, and terminates on energy cutoff,
  per-track max steps, escape, or natural end. Energy is deposited locally on
  every stop except escape (leakage), so showers conserve energy exactly.
- `giant rollout` CLI: seed from real events (argmax pre_E), load checkpoint,
  write a world-frame steps parquet + YAML sidecar.
- giant/analysis.py: compute_rollout_observables + plot_rollout_* for
  single-sided longitudinal/transverse/total-energy shower profiles;
  analysis/export_rollout_observables.py driver.
- scikit-learn added as an optional `geometry` extra (lazy-imported).
- Tests: tests/test_geometry.py, tests/test_rollout.py.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

scripts/dwarf.py

@@ -20,6 +20,7 @@ from scripts.bump_dataset_version import (
     run_update_manifest,
 )
 from scripts.create_root_files import run_make_root
+from scripts.geometry_oracle import run_build_geometry_oracle
 from scripts.hparam_scan import DATA_DEFAULT, SCAN_DIR_DEFAULT, run_hparam_scan
 from scripts.migrate_geant_steps import run_migration
 from scripts.steps_to_parquet import convert_steps_to_parquet
@@ -385,6 +386,51 @@ def make_root(
     )
 
 
+class OracleMethod(str, Enum):
+    knn = "knn"
+    svm = "svm"
+
+
+@app.command("build-geometry-oracle")
+def build_geometry_oracle(
+    data: Annotated[
+        Path, typer.Argument(help="Steps parquet file or directory of steps files")
+    ],
+    out: Annotated[
+        Path, typer.Option("--out", "-o", help="Output oracle .pkl path")
+    ],
+    method: Annotated[
+        OracleMethod,
+        typer.Option("--method", help="Classifier: knn (default) or svm"),
+    ] = OracleMethod.knn,
+    k: Annotated[
+        int, typer.Option("--k", help="Neighbours for the knn classifier")
+    ] = 1,
+    subsample: Annotated[
+        int,
+        typer.Option("--subsample", help="Max reference points sampled from the data"),
+    ] = 500_000,
+    escape_factor: Annotated[
+        float,
+        typer.Option(
+            "--escape-factor",
+            help="escape_threshold = this x median NN spacing of reference points",
+        ),
+    ] = 5.0,
+    seed: Annotated[int, typer.Option("--seed", help="Sampling seed")] = 0,
+) -> None:
+    """Fit a position -> (material, layer_id) oracle for `giant rollout`."""
+    run_build_geometry_oracle(
+        data=data,
+        out=out,
+        method=method.value,
+        k=k,
+        subsample=subsample,
+        escape_factor=escape_factor,
+        seed=seed,
+    )
+
+
 @app.command("hparam-scan")
 def hparam_scan(
     data: Annotated[str, typer.Option("--data")] = DATA_DEFAULT,