Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simulated Thick, Fully-Depleted CCD Exposures Analyzed with Deep
  Learning Techniques   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.

35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th