TPCpp-10M: Simulated proton–proton collisions in Time Projection Chamber for AI Foundation Models

TPCpp-10M Dataset A large-scale particle physics dataset designed for machine learning applications in high-energy physics research. Dataset Overview The TPCpp-10M dataset contains simulated particle collision events from a Time Projection Chamber (TPC) detector, commonly used in particle physics experiments. This dataset is specifically designed for: Foundation model training for particle and nuclear physics applications Particle track reconstruction Particle identification Signal vs. noise discrimination Dataset Characteristics Total Events: over 10 million simulated proton-proton collision events at 200GeV Input Data Format: Structured spacepoint data with 4D coordinates (energy and x,y,z positions) Labels: including track IDs, particle types, and noise classification Splits: unlabeled pretrain: 10M labeled train: 70k labeled events labeled validation: 13k, labeled test: 7k Data type: Numpy compressed data (.npz) Data Components Spacepoints: 4D spacepoints with energy and spatial coordinates Track IDs (only for labeled data): Unique identifiers for particle trajectories Particle ID Labels (only for labeled data): Classification labels for different particle types Noise Tags (only for labeled data): Binary labels distinguishing signal from detector noise Scripts demo.ipynb: data loading, interactive plots, statistics of the dataset plot.py: plot functions Publication FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics (https://arxiv.org/abs/2508.14087)