TomOpt: Muon Tomography experiment optimization

  • Maxime Lagrange MODE Collaboration; Centre for Cosmology, Particle Physics and Phenomenology (CP3), Universite Catholique de Louvain, Louvain la Neuve, Belgium
  • Giles C. Strong MODE Collaboration; Centre for Cosmology, Particle Physics and Phenomenology (CP3), Universite Catholique de Louvain, Louvain la Neuve, Belgium
  • Anna Bordignon Department of Statistical Sciences, University of Padova, Italy
  • Florian Bury University of Bristol, UK
  • Tommaso Dorigo MODE Collaboration; Istituto Nazionale di Fisica Nucleare, Sezione di Padova, Italy
  • Andrea Giammanco MODE Collaboration; Centre for Cosmology, Particle Physics and Phenomenology (CP3), Universite Catholique de Louvain, Louvain la Neuve, Belgium
  • Mariam Heikal American University of Beirut, Beirut, Lebanon
  • Max Lamparth MODE Collaboration; Physik-Department, Technische Universit¨at M¨unchen, Germany
  • Federico Nardi MODE Collaboration; Universite Clermont-Auvergne, Clermont-Ferrand, France; Department of Physics and Astronomy, University of Padova, Italy
  • Aitor Orio Muon Tomography Systems S.L., Bilbao, Spain; Universidad de Cantabria (UC), Santander, Spain
  • Pietro Vischia MODE Collaboration; Universidad de Oviedo and ICTEA, Oviedo, Spain
  • Haitham Zaraket MODE Collaboration; Multi-Disciplinary Physics Laboratory, Optics and Fiber Optics Group, Faculty of Sciences, Lebanese University, Lebanon; Laboratoire de Physique Subatomique et de Cosmologie, Universite Grenoble-Alpes, CNRS/IN2P3, Grenoble, France
DOI: https://doi.org/10.31526/jais.2024.492
Keywords: machine learning, artificial intelligence, computer science

Abstract

The Tomopt software is a tool to optimize the geometrical layout and specifications of detectors designed for muon scattering tomography. Based on differentiable programming techniques, Tomopt consists in a modular pipeline that models all the aspects of a muon tomography task, from the generation and interaction of cosmic ray muons with a parameterized detector and passive material, to the inference on the volume properties. This enables the optimization of the detector parameters via gradient descent, to suggest optimal detector configurations and specifications. This optimisation is subjected to various external constraints such as cost, logistic and material identification efficiency.

Published
2024-04-02
How to Cite
[1]
M. Lagrange, “TomOpt: Muon Tomography experiment optimization”, Journal of Advanced Instrumentation in Science, vol. 2024, no. 1, Apr. 2024.
Issue
Volume 2024
Section
International Workshop on Cosmic-Ray Muography (Muography2023), Naples, Italy

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