Algorithms for Achieving Subpixel Resolution in Muon Tomography
We show that machine learning methods produce superior particle position reconstruction accuracy in scintillation-based detectors.
physics.ins-det↗
arXiv subjects
JungHyun Bae
Publications and source records attributed to JungHyun Bae.
Machine learning methods for subpixel trajectory reconstruction in discretized position detectors
In this study, we demonstrate that compared with traditional centroid-based methods, machine learning methods (particularly transformer-based architectures) achieve superior subpixel position and therefore angular resolution in discretized particle detectors. Using Geant4 Monte Carlo simulated cosmic ray muon data from an 8x8 segmented scintillator detector array, we compare four reconstruction approaches: transformer neural networks, convolutional neural networks, linear regression, and energy-weighted centroids. The transformer architecture achieves the best angular reconstruction with a root mean square error of 1.14{\deg} and a position mean absolute error of 0.24 cm, representing improvements of 2.22x and 6.33x, respectively, over the centroid method. These results enable precise particle trajectory reconstruction for applications in muon tomography and cosmic ray detection.
physics.ins-det↗
Using Cosmic Ray Muons to Assess Geological Characteristics in the Subsurface
Cosmic rays are energetic nuclei and elementary particles that originate from stars and intergalactic events. The interaction of these particles with the upper atmosphere produces a range of secondary particles that reach the surface of the earth, of which muons are the most prominent. With enough energy, muons can travel up to a few kilometers beneath the surface of the earth before being stopped completely. The terrestrial muon flux profile and associated zenith angle can be utilized to determine geological characteristics of a location without having to use conventional methods. This work intends to use a low-power plastic scintillator-based muon detection system for this non-destructive geological assay methodology. 4 custom designed plastic scintillation panels are used to realize two orthogonal detection planes. Simultaneous triggers between detectors from two planes indicate a coincidence event which is recorded using a data acquisition system from FNAL. In order to quantify the systematic uncertainties associated with the detector, such as energy depositions and angular resolution of the detector design, a Monte Carlo simulation using Geant4 is being developed. Simulated and experimental data will drive the development and validation of a reconstruction algorithm that, upon completion, is expected to predict average overburden and rock density. Extended detector exposure to muons can be used as a means to understand changes in the surrounding environment like rock porosity. On the experimental front, the measured flux data will be used to benchmark independent and established models. Successful proof-of-concept demonstration of this technology can open doors for long term non-invasive geological monitoring. The detector design, and experimental methodology are detailed in this work.
physics.ins-det↗
A New Momentum-Integrated Muon Tomography Imaging Algorithm
For decades, the application of muon tomography to spent nuclear fuel (SNF) cask imaging has been theoretically evaluated and experimentally verified by many research groups around the world, including Los Alamos National Laboratory in the United States, Canadian Nuclear Laboratory in Canada, the National Institute for Nuclear Physics in Italy, and Toshiba in Japan. Although monitoring of SNF using cosmic ray muons has attracted significant attention as a promising nontraditional nondestructive radiographic technique, the wide application of muon tomography is often limited because of the natural low cosmic ray muon flux at sea level: 100 m-2min-1sr-1. Recent studies suggest measuring muon momentum in muon scattering tomography (MST) applications to address this challenge. Some techniques have been discussed; however, an imaging algorithm for momentum-coupled MST had not been developed. This paper presents a new imaging algorithm for MST which integrates muon scattering angle and momentum in a single M-value. To develop a relationship between muon momentum and scattering angle distribution, various material samples (Al, Fe, Pb, and U) were thoroughly investigated using a Monte Carlo particle transport code GEANT4 simulation. Reconstructed images of an SNF cask using the new algorithm are presented herein to demonstrate the benefit of measuring muon momentum in MST. In this analysis a missing fuel assembly (FA) was located in the dry storage cask.
physics.ins-det↗