Document Type
Article
Abstract
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.
Digital Object Identifier (DOI)
Publication Info
Reprinted from Hard X-Ray, Gamma-Ray, and Neutron Detector Physics XXIII, Volume 11838, 2021, pages 1183806-.
Copyright 2021 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
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
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. https://doi.org/10.1117/12.2596456