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 (Strong et al 2024 available at: https://github.com/GilesStrong/tomopt ).

Keywords

• detector design
• differentiable programming
• differentiable optimisation
• end-to-end optimisation
• muon tomography
• inference aware learning