  Using detailed simulations of calorimeter showers as training data, we
investigate the use of deep learning algorithms for the simulation and
reconstruction of particles produced in high-energy physics collisions. We
train neural networks on shower data at the calorimeter-cell level, and show
significant improvements for simulation and reconstruction when using these
networks compared to methods which rely on currently-used state-of-the-art
algorithms. We define two models: an end-to-end reconstruction network which
performs simultaneous particle identification and energy regression of
particles when given calorimeter shower data, and a generative network which
can provide reasonable modeling of calorimeter showers for different particle
types at specified angles and energies. We investigate the optimization of our
models with hyperparameter scans. Furthermore, we demonstrate the applicability
of the reconstruction model to shower inputs from other detector geometries,
specifically ATLAS-like and CMS-like geometries. These networks can serve as
fast and computationally light methods for particle shower simulation and
reconstruction for current and future experiments at particle colliders.
