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, $^{135}$Xe, 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.
X-ray Response of the Fully-Depleted, p-Channel SiSeRO-CCD
University of Chicago（芝加哥大学）；Fermi National Accelerator Laboratory（费米国家加速器实验室）；Comisión Nacional de Energía Atómica (CNEA) & Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba（阿根廷国家原子能委员会（CNEA）与阿根廷国家科学研究委员会（CONICET），科尔多瓦国立大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；MIT Lincoln Laboratory（麻省理工学院林肯实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
First evaluation of the tracking performance of the ARCADIA Fully Depleted MAPS with high-energy proton beam
University of Padova（帕多瓦大学）；Istituto Nazionale di Fisica Nucleare Sezione di Padova（意大利国家核物理研究所帕多瓦分部）；Centro di ateneo di Studi e Attività Spaziali CISAS "G. Colombo"（CISAS G. Colombo 大学中心太空研究与活动研究中心）；Fermi National Accelerator Laboratory（费米国家加速器实验室）
