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

Authors :
Giles C Strong
Maxime Lagrange
Aitor Orio
Anna Bordignon
Florian Bury
Tommaso Dorigo
Andrea Giammanco
Mariam Heikal
Jan Kieseler
Max Lamparth
Pablo Martínez Ruíz del Árbol
Federico Nardi
Pietro Vischia
Haitham Zaraket
Source :
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035002 (2024)
Publication Year :
2024
Publisher :
IOP Publishing, 2024.

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 ).

Details

Language :
English
ISSN :
26322153
Volume :
5
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Machine Learning: Science and Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.b5fd5028d124413e83af68a527d8d56d
Document Type :
article
Full Text :
https://doi.org/10.1088/2632-2153/ad52e7