10 Million Particle Events: Enabling Foundation Models for Sparse 3D Inverse Problems

Next-generation particle physics experiments require unprecedented machine learning capabilities to achieve their science goals. We propose generating 10 million particle detector events, the first dataset providing raw sensor waveforms paired with 3D ground truth at scale, enabled by GPU-accelerated JAX simulations achieving two orders of magnitude speedup over traditional CPU-based tools. This dataset will enable large-scale self-supervised training of foundation models for complex inverse problems in the particle physics and beyond.