An Application of CNNs to Time Sequenced One Dimensional Data in Radiation Detection
This work addresses isotope classification for security or environmental radiation detection, representing an incremental application of existing image-based CNN techniques to a new domain-specific data type.
The paper tackled the problem of classifying isotopes from time-sequenced gamma-ray spectra by applying a Convolutional Neural Network (CNN) to interpret the data as monochromatic images, allowing the network to discover time-sequenced features instead of relying on arbitrary time binning used in conventional methods.
A Convolutional Neural Network architecture was used to classify various isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation detection system of a type commonly fielded for security or environmental measurement purposes. A two-dimensional surface (waterfall plot) in time-energy space is interpreted as a monochromatic image and standard image-based CNN techniques are applied. This allows for the time-sequenced aspects of features in the data to be discovered by the network, as opposed to standard algorithms which arbitrarily time bin the data to satisfy the intuition of a human spectroscopist. The CNN architecture and results are presented along with a comparison to conventional techniques. The results of this novel application of image processing techniques to radiation data will be presented along with a comparison to more conventional adaptive methods.