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Fits normalizers over multi-hundred-million-row datasets, so the setup pass's per-row Python overhead compounds fast: encode_secondaries recomputed an O(K) prefix sum from scratch on every one of its 15 stick-breaking iterations, np.isin re-sorted the full train-event-id array on every chunk, and pdg/material/process index lookups ran a Python dict lookup per row. The normalizer-fit pass also computed encode_secondaries's stick-logit and direction-rotation blocks in full even though it only ever reads the mass/charge columns. Replace the prefix-sum recompute with a single np.cumsum, add a sorted_membership helper (searchsorted-based) in place of np.isin at both the setup-pass and per-epoch call sites, vectorize the index lookups via _vectorized_map_lookup, and add an opt-in phys_only path so the setup pass skips the stick-breaking/rotation work it discards anyway. All four changes are output-identical performance refactors, backed by new unit tests plus the existing suite. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>