GAN-based models to fast-simulate the LHCb PID detectors

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License: GNU General Public License v3 (GPLv3) (GPLv3 License)

Author: Matteo Barbetti

Tags tensorflow, machine learning, deep learning, generative adversarial nets, lhcb experiment, lamarr, ultra-fast simulation, particle identification

Requires: Python <=3.11, >=3.7

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PIDGAN

GAN-based models to fast-simulate the LHCb PID detectors

TensorFlow versions scikit-learn versions Python versions PyPI - Version GitHub - License

GitHub - Tests Codecov

GitHub - Style Code style: black

Generative Adversarial Networks

Algorithms* Implementation Test Paper
GAN ✅ ✅ arXiv:1406.2661
BceGAN ✅ ✅
LSGAN ✅ ✅ arXiv:1611.04076
WGAN ✅ ✅ arXiv:1701.07875
WGAN_GP ✅ ✅ arXiv:1704.00028
CramerGAN ✅ ✅ arXiv:1705.10743
WGAN_ALP ✅ ✅ arXiv:1907.05681

*Designed to operate according to the conditional version proposed in arXiv:1411.1784

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License: GNU General Public License v3 (GPLv3) (GPLv3 License)

Author: Matteo Barbetti

Tags tensorflow, machine learning, deep learning, generative adversarial nets, lhcb experiment, lamarr, ultra-fast simulation, particle identification

Requires: Python <=3.11, >=3.7

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