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

TL;DR

TomOpt is a novel software tool that uses differentiable programming to optimize the design of muon tomography detectors, enabling end-to-end, inference-aware optimization of particle physics instruments.

Contribution

It introduces the first demonstration of end-to-end differentiable and inference-aware optimization for particle physics detector design.

Findings

Effective optimization of detector layouts demonstrated on benchmark scenarios

Potential for improved detector performance and design efficiency

Open-source code available for community use

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.