Journal
EUROPEAN PHYSICAL JOURNAL C
Volume 80, Issue 7, Pages -Publisher
SPRINGER
DOI: 10.1140/epjc/s10052-020-8251-9
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Funding
- United States Department of Energy, Office of High Energy Physics Research under Caltech [DE-SC0011925]
- Office of High Energy Physics HEP-Computation
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [772369]
- Zhejiang University/University of Illinois Institute Collaborative Research Program [083650]
- National Science Foundation [OCI-0725070, ACI-1238993]
- State of Illinois
- NVIDIA
- Kavli Foundation
- SuperMicro
- Caltech
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Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle 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.
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