TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
Abstract: We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In doing so, we provide the first demonstration of end-to-end-differentiable and inference-aware optimisation of particle physics instruments. We study the performance of the software on a relevant benchmark scenario and discuss its potential applications. Our code is available on Github.
- John Wiley & Sons, 2022.
- https://www.iaea.org/publications/15182/muon-imaging.
- T. K. Gaisser, Cosmic rays and particle physics. Cambridge University Press, 1990.
- Curran Associates, Inc., 2019. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf.
- 1981.
- J. Hadamard, Mémoire sur le problème d’analyse relatif à l’équilibre des plaques élastiques encastrées. Mémoires présentés par divers savants à l’Académie des sciences de l’Institut de France: Éxtrait. Imprimerie nationale, 1908. http://books.google.com.au/books?id=BTEPAAAAIAAJ.
- 2015. http://arxiv.org/abs/1412.6980.
- 2017. arXiv:1506.01186. http://arxiv.org/abs/1506.01186.
- SPIE, 2019. arXiv:1708.07120. https://doi.org/10.1117/12.2520589.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.