We present gato-hep: the Gradient-based cATegorization Optimizer for High Energy Physics analyses. gato-hep learns boundaries in N-dimensional discriminants that maximize signal significance for binned likelihood fits, using a differentiable approximation of signal significance and gradient descent techniques for optimization with TensorFlow.

• 📘 Documentation: https://gato-hep.readthedocs.io/
• 📦 PyPI: https://pypi.org/project/gato-hep/
• 🧪 Examples: see the examples/ directory in this repository

Key Features

• Optimize categorizations in multi-dimensional spaces using Gaussian Mixture Models (GMM) or 1D sigmoid-based models
• Set the range of the discriminant dimensions as needed for your analysis
• Penalize low-yield or high-uncertainty categories to keep optimizations analysis-friendly
• Built-in annealing schedules for temperature / steepness (setting the level of approximation for differentiability), and learning rate to stabilize training
• Ready-to-run toy workflows that mirror real HEP analysis patterns

Installation

Latest release (PyPI)

pip install gato-hep

The base install targets CPU execution and pulls the tested TensorFlow stack automatically. Optional extras:

pip install "gato-hep[gpu]"   # CUDA-enabled TensorFlow wheels

For the GPU extra you still need NVIDIA drivers and CUDA libraries that match the selected TensorFlow build.

From source

git clone https://github.com/FloMau/gato-hep.git
cd gato-hep
python -m venv .venv  # or use micromamba/conda
source .venv/bin/activate
pip install -e ".[dev]"

Requirements: Python ≥ 3.10. See pyproject.toml for the authoritative dependency pins.

Quickstart

The snippet below mirrors the three-class softmax demo. It generates the 3D toy sample, fits a two-dimensional Gaussian mixture model to the softmax scores, and reports the per-signal significances produced by the learnt categories.

import numpy as np
import tensorflow as tf
from pathlib import Path

from gatohep.data_generation import generate_toy_data_3class_3D
from gatohep.models import gato_gmm_model


def convert_data_to_tensors(data):
    tensors = {}
    for proc, df in data.items():
        scores = np.stack(df["NN_output"].values)[:, :2]  # keep the first two dims
        weights = df["weight"].values
        tensors[proc] = {
            "NN_output": tf.convert_to_tensor(scores, tf.float32),
            "weight": tf.convert_to_tensor(weights, tf.float32),
        }
    return tensors

# setup class for the 2D discriminant optimization
class SoftmaxGMM(gato_gmm_model):
    def __init__(self, n_cats, temperature=0.3):
        super().__init__(
            n_cats=n_cats,
            dim=2,
            temperature=temperature,
            mean_norm="softmax",
        )
    def call(self, data_dict):
        # Differentiate through the Asimov significances provided by the helper
        significances = self.get_differentiable_significance(
            data_dict,
            signal_labels=["signal1", "signal2"],
        )
        z1 = significances["signal1"]
        z2 = significances["signal2"]
        return -tf.sqrt(z1 * z2)  # geometric-mean loss

# load your data as dictionary containing pandas DataFrames, or use the integrated toy data generation:
data = generate_toy_data_3class_3D()
tensors = convert_data_to_tensors(data)

# example: use 10 bins
model = SoftmaxGMM(n_cats=10, temperature=0.3)
optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.05)

# actual training
for epoch in range(100):
    with tf.GradientTape() as tape:
        loss = model.call(tensors)
    grads = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

# Save the trained model for later use in the analysis to some path
checkpoint_path = Path("softmax_demo_ckpt")
model.save(checkpoint_path)

# Restore the model
restored = SoftmaxGMM(n_cats=10, temperature=0.3)
restored.restore(checkpoint_path)

# Obtain the hard (non-differentiable) bin assignments
assignments = restored.get_bin_indices(tensors)

See examples/three_class_softmax_example/run_example.py for the full training loop with schedulers, plotting helpers, and GIF generation.

Examples & Tutorials

• examples/1D_example/run_sigmoid_example.py – sigmoid-based boundaries for a single discriminant.
• examples/1D_example/run_gmm_example.py – GMM-based categorisation for the same data.
• examples/three_class_softmax_example/run_example.py – optimize categories directly on a 3-class softmax output (shown in 2D projections).
• examples/bumphunt_example/run_example.py – $H\to\gamma\gamma$–style bump hunt example with inference on the mass, but including the background over a wider range for increased statistical power.

Every script populates an examples/.../Plots*/ folder with plots and checkpoints.

Further Reading

• Full documentation, including the API reference: https://gato-hep.readthedocs.io/
• Issues & feature requests: https://github.com/FloMau/gato-hep/issues

Contributing

1. Fork and branch: git checkout -b feature/xyz.
2. Implement changes under src/gatohep/ and possibly add/adjust tests in tests/.
3. Format and lint (flake8) and run pytest.
4. Open a pull request summarizing the physics motivation and technical changes.

License

MIT License © Florian Mausolf