Machine Learning for Muon Imaging

Abstract

Muon imaging is a new imaging technique which can be used to image large bulky objects, especially objects with heavy shielding where other techniques like X-ray CT scanning will often fail. This is due to the fact that high energy cosmic rays have a very high penetrative power and can easily penetrate hundreds of meters of rock. Muon imaging is essentially an inverse problem. There are two popular forms of muon imaging techniques - absorption muon imaging based on the attenuation of muons in matter and multiple scattering muon imaging based on the multiple scattering effect of muons. Muon imaging can be used in many areas, ranging from volcanology and searching for secret cavities in pyramids over border monitoring for special nuclear materials to nuclear safeguards applications for monitoring the spent fuel casks. Due to the lack of man-made muon sources, both of the muon imaging techniques rely on cosmic ray muons. One important shortcoming of comic ray muons are the very low intensities. In order to get high image resolutions, very long exposure times are needed. In this paper, we will study how machine learning techniques can be used to improve the muon imaging techniques.