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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Differential optimisation for task- and constraint-aware design of particle detectors

Giles Strong · Maxime Lagrange · Aitor Orio Alonso · Anna Bordignon · Florian Bury · tommaso dorigo · Andrea Giammanco · Mariam Safieldin · Jan Kieseler · Max Lamparth · Pablo Martinez · Federico Nardi · Pietro Vischia · Haitham Zaraket


We describe a software package, 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 modelling 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.

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