Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
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Updated
Oct 22, 2024 - C++
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
Celeritas is a new Monte Carlo transport code designed to accelerate scientific discovery in high energy physics by improving detector simulation throughput and energy efficiency using GPUs.
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
Conda recipe files for the Fermi Sciencetools software analysis package: Fermitools
Julia package for particle 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.
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