Using Convolutional Neural Networks to Classify Scintillator Data

Isomeric states are sensitive to changes in nuclear structure. Being able to observe evidence of isomeric transitions is important to expanding our knowledge of nuclear structure as a whole. A monolithic inorganic planar scintillator coupled to a position-sensitive photomultiplier tube readout is used to record the energy deposition in the detector through the light emission due to β particles, internal conversion electrons, and γ rays. The positions and energies of which are associated with previously identified nuclei to allow introspection into changes in the structure of the nucleus. Of particular interest is the detection of two interactions within the scintillator that occur separated in time and/or space, which are currently unrecoverable by the current analysis pipeline. This drives the development of a method to produce artificial experimental data to be used as training data for convolutional neural networks. This method has promising results in classifying single and multiple interactions in artificial scintillator data.