hep-lbdl/CaloGAN
Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation
This project helps high-energy physicists significantly speed up simulations of particle showers in multi-layer electromagnetic calorimeters at facilities like CERN. It takes high-energy particle shower data, typically from GEANT4 simulations, and uses a deep learning model to generate new, realistic simulation outputs much faster than traditional methods. Experimental physicists, computational physicists, and researchers working with particle detector simulations are the primary users.
112 stars. No commits in the last 6 months.
Use this if you need to generate high-fidelity simulations of 3D particle showers in calorimeters at a fraction of the computational cost of traditional methods.
Not ideal if you are not working with high-energy physics detector simulations or if you require direct, non-generative GEANT4 output.
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Jun 08, 2024
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