# giant

**G**eant4 **I**nference via **A**utoregressive **N**eural s**T**ep surrogate — a play on *Geant4* and the step function being the computationally heaviest part of the simulation.

Proof-of-concept surrogate model for the Geant4 step function. Given a pre-step particle state, the model samples a physically plausible post-step outcome — replacing the stochastic Geant4 physics engine with a trained conditional generative model.

Training is driven entirely from parquet files of the miniCaloSim steps tree. No Geant4 runtime dependency.

## Architecture

Conditional **flow matching** model (Lipman et al. 2022): a small MLP learns a vector field mapping noise → step outcomes in ~10 ODE steps per sample. Falls back to DDPM for comparison.

**Output space (9D, diffused):**

| Index | Variable | Transform |
|-------|----------|-----------|
| 0 | `step_length` [mm] | log |
| 1 | `ΔE = pre_E − post_E` [MeV] | log |
| 2 | `edep` [MeV] | log |
| 3–5 | `post_dir` in local frame | unit vector |
| 6–8 | `travel_dir` (`post_pos − pre_pos`) in local frame | unit vector |

Both `post_dir` and `travel_dir` are expressed in the coordinate frame where `pre_dir = ẑ`, making the scattering distribution nearly azimuthally symmetric. `post_pos` itself is not a raw target — it's reconstructed at inference as `pre_pos + step_length * world_frame(travel_dir)`, so the two stay consistent by construction instead of being learned (and potentially diverging) independently.

**Conditioning:** PDG code (embedding), pre-step position, log(pre-energy), pre-step direction, material (embedding), layer ID, number of secondaries (Phase 1 only — see Roadmap below).

## Roadmap

The model is developed in two phases:

**Phase 1 (current):** The number of secondaries produced in each step is passed as a conditioning input. This makes training easier because the model has direct access to multiplicity information and can focus on learning the continuous post-step kinematics.

**Phase 2 (target):** The number of secondaries is not given — the model must predict it jointly with all secondary properties (energy, direction, species) for each step. This requires extending the output space and likely an autoregressive or set-based generative approach for the variable-length secondary list.

## Data

Input: parquet files produced by [miniCaloSim](https://gitlab.etp.kit.edu/lbogner/minicalosim), or converted from a ROOT file via `scripts/steps_to_parquet.py`. Each row is one Geant4 step. Train/val split is by `event_id` (not row shuffle) to avoid leaking correlated steps from the same shower.

## Project structure

```
giant/
├── giant/
│   ├── data/
│   │   ├── loader.py       # parquet → numpy arrays (incl. streaming/chunked reads)
│   │   ├── transforms.py   # log transforms, local-frame rotation, normaliser
│   │   └── dataset.py      # StepsDataset / StreamingStepsDataset (PyTorch)
│   ├── model/
│   │   ├── network.py      # SinusoidalEmbedding, ConditionEncoder, DenoisingMLP
│   │   └── schedule.py     # CosineSchedule (DDPM) and flow matching utilities
│   ├── config.py           # default hyperparameters, TOML config merging, device autodetect
│   ├── pipeline.py         # builds datasets/normalizers and kicks off a training run
│   ├── train.py            # training loop, checkpointing, graceful shutdown
│   ├── sample.py           # DDPM / DDIM / flow matching samplers
│   ├── validate.py         # step-level marginal + KL-divergence validation
│   ├── analysis.py         # notebook diagnostics: marginals, correlations, constraint checks
│   └── cli.py              # `giant train` / `giant predict` Typer app
├── scripts/
│   ├── train.py              # argparse training entry point
│   └── steps_to_parquet.py   # ROOT → parquet conversion (uproot/awkward/polars)
└── tests/
```

## Setup

```bash
uv sync --extra cpu               # CPU-only torch (use --extra cuda for CUDA 11.8 instead)
uv sync --extra cpu --extra dev   # add dev tools (pytest, ruff, ty)
```

`cpu` and `cuda` are mutually exclusive extras selecting the torch build; plain `uv sync` installs no torch at all. See `CLAUDE.md` for details.

## Training

```bash
python scripts/train.py --data path/to/steps.parquet --mode flow
```

or via the installed CLI:

```bash
giant train path/to/steps.parquet --mode flow
giant predict path/to/steps.parquet --checkpoint checkpoints/.../best.pt
```

Both accept a TOML config file (`--config`) and CLI overrides for hyperparameters; see `--help` on either for the full option list.

## Validation

`giant.validate.validate_marginals` runs step-level marginal and KL-divergence checks during training (`--validate-every`). For deeper, notebook-driven diagnostics on a trained checkpoint — stratified marginals, correlation structure, physical constraint violations — see `giant.analysis`.

## Development

```bash
uv run pytest            # run tests
uv run ruff check .      # lint
uv run ruff format .     # format
uv run ty check .        # type check
```