2022/01/22 by C. Britt, Britt, C., E. Church +11 · 1 citation
Physics and Astronomy · #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Nuclear Experiment (nucl-ex) #Nuclear Physics and Applications #Particle Detector Development and Performance #Radiation Detection and Scintillator Technologies
paper · pdf · doi:10.48550/arxiv.2201.08973
openalex publication_date 2022/01/22 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
Thick, Charge Coupled Devices (CCDs) have recently been explored for applied physics, such as nuclear explosion monitoring, and dark matter detection purposes. When run in fully-depleted mode, these devices are sensitive detectors for energy depositions by a variety of primary particles. In this study we are interested in applying the Deep Learning (DL) technique known as panoptic segmentation to simulated CCD images to identify, attribute and measure energy depositions from radioisotopes of interest. We simulate CCD exposures of a chosen radioxenon isotope, 135Xe, and overlay a simulated cosmic muon background appropriate for a surface-lab. We show that with this DL technique we can reproduce the beta spectrum to good accuracy, while suffering expected confusion with same-topology gammas and conversion electrons and identifying cosmic muons less than optimally.