Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303

pure-Python HistFactory implementation with tensors and autodiff

Model manipulation and fitting library based on TensorFlow and optimised for simple and direct manipulation of probability density functions. Its main focus is on scalability, parallelisation and user friendly experience.

Machine Learning for High Energy Physics.

Metapackage of Scikit-HEP project data analysis packages for Particle Physics.

Package to deal with particles, the PDG particle data table, PDGIDs, etc.

Header only framework for data analysis in massively parallel platforms.

Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation

Native Julia I/O package to work with CERN ROOT files objects (TTree and RNTuple)

A Deep learning library for neutrino telescopes

A Python package for flavour physics phenomenology in the Standard model and beyond

Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics

Standalone monitor for process resource consumption

Package to parse decay files, describe and convert particle decays between digital representations.

ATLAS Run 2 and Run 3 analysis framework for AnalysisTop and AnalysisBase for proton-smashing physics

A HEP Program for Flavor Observables

Conda recipe files for the Fermi Sciencetools software analysis package: Fermitools

Julia package for particle physics

Personal notes on statistics with a focus on applications to experimental high energy physics

A Google Summer of Code 2021 Project Repository. This project aims to demonstrate quantum machine learning's potential, specifically Quantum Convolutional Neural Network (QCNN), in HEP events classification from particle image data. The code used in the research is wrapped as an open-source package to ease future research in this field.