Datasets for fitting trajectories of elementary particles using deep learning

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:datasets.zip*: 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). checkpoints.zip: checkpoint_rnn.pth: PyTorch weights of the RNN trained model. checkpoint_transformer_encoder.pth: PyTorch weights of the RNN trained model. *This dataset corrects a bug present in previous versions related to the true initial position and momentum of the particles, which were not used in this study; therefore, the trajectory fitting results reported in the manuscript are unaffected.