Three-Body Hidden Forces Benchmark Dataset: 1,200+ Simulated Chaotic Trajectories with Controlled Perturbations

This dataset contains over 1,200 high-resolution simulated three-body dynamical trajectories generated for anomaly detection and scientific machine learning research. It includes clean Newtonian simulations alongside five categories of controlled unmodeled perturbations: hidden mass, non-inverse-square gravitational interactions, drag forces, impulsive disturbances, and time-varying gravitational constants. Each sample is stored in compressed NumPy (.npz) format and contains trajectory state data (positions and velocities) suitable for temporal modeling and representation learning. The dataset is designed to benchmark anomaly detection methods, particularly Neural ODEs and dynamical latent models, in chaotic nonlinear systems where hidden or unmodeled forces may alter expected behavior. It can be used for supervised anomaly classification, out-of-distribution generalization studies, and scientific AI experimentation in chaotic dynamical systems.