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Muon event localisation with AI

Journal Article


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.

Publication Date


  • 2021

Citation


  • Heredge, J., Archer, J. W., Duffy, A. R., Brown, J. M. C., Guatelli, S., Scutti, F., . . . Webster, C. (2021). Muon event localisation with AI. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1001. doi:10.1016/j.nima.2021.165237

Scopus Eid


  • 2-s2.0-85103989651

Volume


  • 1001

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.

Publication Date


  • 2021

Citation


  • Heredge, J., Archer, J. W., Duffy, A. R., Brown, J. M. C., Guatelli, S., Scutti, F., . . . Webster, C. (2021). Muon event localisation with AI. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1001. doi:10.1016/j.nima.2021.165237

Scopus Eid


  • 2-s2.0-85103989651

Volume


  • 1001