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The Maximum Likelihood-Expectation Maximization (ML-EM) method was applied to 3D image reconstruction of cosmic-ray muon tomography. The feasibility was examined by using Monte Carlo simulation for a simple configuration where two lead blocks were placed at a different height from a muography detector. The 2D projection of the average thickness of the blocks as a function of the muon direction was simulated for multiple detection positions. The 3D image of the density profile was reconstructed by applying the ML-EM method to the simulated projections. It was found that the image reproduces reasonably well the position of the two blocks. The effect of the limited number of detection positions and the number of iteration in the ML-EM method on the image reconstruction was investigated in detail.

Using a setup for testing a prototype for a satellite-borne cosmic-ray ion detector, we have operated a stack of scintillator and silicon detectors on top of the Princess Sirindhorn Neutron Monitor (PSNM), an NM64 detector at 2560-m altitude at Doi Inthanon, Thailand (18.59 • N, 98.49 • E). Monte Carlo simulations have indicated that about 15% of the neutron counts by PSNM are due to interactions (mostly in the lead producer) of GeV-range protons among the atmospheric secondary particles from cosmic ray showers, which can be detected by the scintillator and silicon detectors. Those detectors can provide a timing trigger for measurement of the propagation time distribution of such neutrons as they scatter and propagate through the NM64, processes that are similar whether the interaction was initiated by an energetic proton (for 15% of the count rate) or neutron (for 80% of the count rate). This propagation time distribution underlies the time delay distribution between successive neutron counts, from which we can determine the leader fraction (inverse multiplicity), which has been used to monitor Galactic cosmic ray spectral variations over ∼1-40 GV. Here we have measured and characterized the propagation time distribution from both the experimental setup and Monte Carlo simulations of atmospheric secondary particle detection. We confirm a known propagation time distribution with a peak (at ≈70 µs) and tail over a few ms, dominated by neutron counts. We fit this distribution using an analytic model of neutron diffusion and absorption, for both experimental and Monte Carlo results. In addition we identify a group of prompt neutron monitor pulses that arrive within 20 µs of the charged-particle trigger, of which a substantial fraction can be attributed to charged-particle ionization in a proportional counter, according to both experimental and Monte Carlo results. Prompt pulses, either due to neutrons or charged-particle ionization, are associated with much higher mean multiplicity than typical pulses. These results validate and point the way to some improvements in Monte Carlo simulations and the resulting yield functions used to interpret the neutron monitor count rate and leader fraction.

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A feasibility demonstration of three-dimensional (3D) muon tomography was performed for infrastructure equivalent targets using the proposed portable muography detector. For the target, we used two sets of lead blocks placed at different heights. The detector consists of two muon position-sensitive detectors, made of plastic scintillating fibers (PSFs) and multi-pixel photon counters (MPPCs) with an angular resolution of 8 msr. In this work, the maximum likelihood-expectation maximization (ML-EM) method was used for the 3D imaging reconstruction of the muography. For both simulation and experiment, the reconstructed positions of the blocks produce consistent results with prior knowledge of the blocks' arrangement. This result demonstrates the potential of the 3D tomographic imaging of infrastructure by using seven detection positions for portable muography detectors to image infrastructure scale targets.