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TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography

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arxiv 2309.14027 v3 pith:FYJ7SC6T submitted 2023-09-25 physics.ins-det hep-exstat.ML

classification physics.ins-dethep-exstat.ML
keywords detectorsoptimisationsoftwaremuonparticletomographytomoptapplications
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MANGO: An Autodiff Neutrino Oscillation Engine for Differentiable Analysis Pipelines

    hep-ex 2026-08 conditional novelty 6.0 of 10

    A fully differentiable neutrino oscillation engine computes exact gradients through layered-Earth geometry and downstream analysis, validated against external codes and finite differences.

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