5 Commits

Include renames

Author	SHA1	Message	Date
larsandClaude Sonnet 5	09e4c765c7	Speed up giant train's setup stage ... 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>	2026-07-29 13:34:32 +02:00
larsandClaude Sonnet 5	68fb99bed8	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>	2026-07-17 15:12:54 +02:00
larsandClaude Sonnet 5	670f57c309	Rescale secondary energies to exactly consume the e_sec budget ... decode_secondaries's stick-breaking only guarantees valid secondary slots sum to <= e_sec, leaving a shortfall that rollout.py silently dumped into that step's edep. Rescale the valid slots by one common per-row factor instead, so they sum to exactly e_sec whenever n_sec > 0: this spreads any shortfall proportionally across all secondaries rather than concentrating it in whichever slot is last by energy rank (which would let that one low-energy secondary balloon and distort the shower's topology). Rows where every valid slot decodes to ~zero fall back to an even split. n_sec == 0 rows are unchanged (still nothing to carry the budget, so rollout.py's edep top-up still applies there) — narrowed the related caveat in load_rollout_vs_truth's docstring to just that case. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-13 13:45:13 +02:00
larsandClaude Opus 4.8	a14a4f973a	Apply ruff format across the codebase ... Whitespace-only reflow (line wrapping, blank lines between defs); no logic changes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-08 14:44:53 +02:00
larsandClaude Sonnet 4.6	e6e0eb22bf	Implement Phase 2: secondary particle prediction ... Two-stage factorisation: Stage 1 predicts 9D primary kinematics + n_sec classification head (COND_DIM reduced to 8, dropping n_sec/e_sec inputs); Stage 2 (SecondaryDecoder) generates K_MAX=15 secondary slots via masked flow matching over (stick_logit, local_dir, type_emb) conditioned on Stage 1 output. Joint training with combined loss L_s1 + λ_nsec*L_nsec + λ_s2*L_s2. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-29 11:34:31 +02:00