Muon event localisation with AI

Journal article (2021)

Authors

J. Heredge Swinburne University of Technology
J. W. Archer University of Wollongong
A. R. Duffy Swinburne University of Technology
J.M.C. Brown University of Wollongong, RST/Medical Physics & Technology - AS
Susanna Guatelli University of Wollongong
F. Scutti University of Melbourne
S. Krishnan Swinburne University of Technology
C Webster Swinburne University of Technology
Research Group
RST/Medical Physics & Technology (AS) (TU Delft)
Scintillators Data processing methods Detector design Particle tracking detectors (solid-state detectors) Radiation calculations
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Applied Sciences

Department

RST/Radiation, Science and Technology

Research Group

RST/Medical Physics & Technology

Abstract

Low-cost muon detectors utilising cheap plastic scintillators and a limited number of individual silicon photomultipliers (SiPMs) offer a compelling approach to cheap experimental designs, provided the event localisation of a traversing particle can be accurately determined. In this theoretical work, we use Geant4 to simulate a diverse range of detector configurations, shapes and SiPM photosensors, predicting the light intensity received at a given SiPM. Testing a range of methods to localise muon events we determine that machine learning techniques outperform analytic models, and of these, a simple gradient boosted framework is the most reliably accurate localisation technique for our simulated scintillators. We find that a simple square scintillator outperforms other geometries and that AI performs, when applied to this shape, with a linear relationship between the positional accuracy of the event recovery and the average distance between photosensors around the detector perimeter.