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We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data.

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APA Citation

Chaudhuri, S. K., Kleppinger, J. W., Nag, R., Roy, K., Panta, R., Agostinelli, F., Sheth, A., Roy, U. N., James, R. B., & Mandal, K. C. (2021). A CdZnTeSe gamma spectrometer trained by deep convolutional neural network for radioisotope identification. Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII, 11838, 1183806.