Datasets for fitting trajectories of elementary particles using deep learning
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Training and testing datasets of simulated elementary particles, used for fitting the trajectories of elementary particles in dense materials immersed in a magnetic field using deep learning. Once decompressed, the directory structure is the following: training (1,762,327 particles in total): proton: 414,824 particles. pion: 432,855 particles. muon: 446,858 particles (muons and antimuons). electron: 467,790 particles (electrons and positrons). testing (1,759,491): proton: 412,092 particles. pion: 432,807 particles. muon: 447,003 particles (muons and antimuons). electron: 467,589 particles (electrons and positrons).
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Zenodo创建时间：
2022-11-24
