gato-hep

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

Quick links

• GitHub: FloMau/gato-hep

• PyPI: https://pypi.org/project/gato-hep/

• Examples: see the examples/ directory in the repository

For setup details see Installation, and jump to the API Reference reference when integrating gato-hep into your analysis.

Contents

• Installation
  • PyPI release
  • Editable install from source
  • Post-install check
  • Environment compatibility
• Examples
  • Specifically:
• API Reference
  • gatohep.models
  • gatohep.losses
  • gatohep.utils
  • gatohep.plotting_utils
  • gatohep.data_generation

Indices and tables

• Module Index

• Index

• Search Page