Muon event localisation with AI

• DOI: 10.1016/j.nima.2021.165237
• Publication Date: 2021-03-25T15:23:58Z
• Authors: J. Heredge; J.W. Archer; A.R. Duffy; J.M.C. Brown; S. Guatelli; F. Scutti; S. Krishnan; C. Webster
• Keywords: 03 medical and health sciences; 0302 clinical medicine; 0103 physical sciences; 01 natural sciences

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.