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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Reprinted from Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII, Volume 11838, 2021, pages 1183806-.
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Sandeep K. Chaudhuri, Joshua W. Kleppinger, Ritwik Nag, Kaushik Roy, Rojina Panta, Forest Agostinelli, Amit Sheth, Utpal N. Roy, Ralph B. James, & Krishna C. Mandal. “A CdZnTeSe gamma spectrometer trained by deep convolutional neural network for radioisotope identification,” Proc. SPIE Volume 11838, Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII, 1183806 (2021). https://doi.org/10.1117/12.2596456
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. https://doi.org/10.1117/12.2596456