  Detector simulation and reconstruction are a significant computational
bottleneck in particle physics. We develop Particle-flow Neural Assisted
Simulations (Parnassus) to address this challenge. Our deep learning model
takes as input a point cloud (particles impinging on a detector) and produces a
point cloud (reconstructed particles). By combining detector simulations and
reconstruction into one step, we aim to minimize resource utilization and
enable fast surrogate models suitable for application both inside and outside
large collaborations. We demonstrate this approach using a publicly available
dataset of jets passed through the full simulation and reconstruction pipeline
of the CMS experiment. We show that Parnassus accurately mimics the CMS
particle flow algorithm on the (statistically) same events it was trained on
and can generalize to jet momentum and type outside of the training
distribution.
