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

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, t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Machine learning: science and technology 2024-09, Vol.5 (3), p.35002
Hauptverfasser: Strong, Giles C, Lagrange, Maxime, Orio, Aitor, Bordignon, Anna, Bury, Florian, Dorigo, Tommaso, Giammanco, Andrea, Heikal, Mariam, Kieseler, Jan, Lamparth, Max, Martínez Ruíz del Árbol, Pablo, Nardi, Federico, Vischia, Pietro, Zaraket, Haitham
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 ).
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/ad52e7