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

Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics