KIT | KIT-Bibliothek | Impressum | Datenschutz

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

Strong, Giles C. ; Lagrange, Maxime ; Orio, Aitor ; Bordignon, Anna; Bury, Florian; Dorigo, Tommaso; Giammanco, Andrea; Heikal, Mariam; Kieseler, Jan 1; Lamparth, Max; Martínez Ruíz del Árbol, Pablo; Nardi, Federico; Vischia, Pietro; Zaraket, Haitham
1 Karlsruher Institut für Technologie (KIT)

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).


Verlagsausgabe §
DOI: 10.5445/IR/1000172923
Veröffentlicht am 19.08.2024
Cover der Publikation
Zugehörige Institution(en) am KIT KIT-Bibliothek (BIB)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 01.09.2024
Sprache Englisch
Identifikator ISSN: 2632-2153
KITopen-ID: 1000172923
Erschienen in Machine Learning: Science and Technology
Verlag Institute of Physics Publishing Ltd (IOP Publishing Ltd)
Band 5
Heft 3
Seiten Art.-Nr.: 035002
Vorab online veröffentlicht am 03.07.2024
Nachgewiesen in Dimensions
Web of Science
Scopus
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page