  Deep learning methods have gained popularity in high energy physics for fast
modeling of particle showers in detectors. Detailed simulation frameworks such
as the gold standard Geant4 are computationally intensive, and current deep
generative architectures work on discretized, lower resolution versions of the
detailed simulation. The development of models that work at higher spatial
resolutions is currently hindered by the complexity of the full simulation
data, and by the lack of simpler, more interpretable benchmarks. Our
contribution is SUPA, the SUrrogate PArticle propagation simulator, an
algorithm and software package for generating data by simulating simplified
particle propagation, scattering and shower development in matter. The
generation is extremely fast and easy to use compared to Geant4, but still
exhibits the key characteristics and challenges of the detailed simulation. We
support this claim experimentally by showing that performance of generative
models on data from our simulator reflects the performance on a dataset
generated with Geant4. The proposed simulator generates thousands of particle
showers per second on a desktop machine, a speed up of up to 6 orders of
magnitudes over Geant4, and stores detailed geometric information about the
shower propagation. SUPA provides much greater flexibility for setting initial
conditions and defining multiple benchmarks for the development of models.
Moreover, interpreting particle showers as point clouds creates a connection to
geometric machine learning and provides challenging and fundamentally new
datasets for the field.
  The code for SUPA is available at https://github.com/itsdaniele/SUPA.
