학술논문

TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
Document Type
Working Paper
Source
Subject
Physics - Instrumentation and Detectors
High Energy Physics - Experiment
Statistics - Machine Learning
Language
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 scenarios and discuss its potential applications.
Comment: V2: Updated author list; 28 pages content