Automated Isotope Identification Algorithm Using Artificial Neural Networks

There is a need to develop an algorithm that can determine the relative activities of radioisotopes in a large data set of low-resolution gamma-ray spectra that contain a mixture of many radioisotopes. Low-resolution gamma-ray spectra that contain mixtures of radioisotopes often exhibit feature overlap, requiring algorithms that can analyze these features when overlap occurs. While machine learning and pattern recognition algorithms have shown promise for the problem of radioisotope identification, their ability to identify and quantify mixtures of radioisotopes has not been studied. Because machine-learning algorithms use abstract features of the spectrum, such as the shape of overlapping peaks and Compton continuum, they are a natural choice for analyzing radioisotope mixtures. An artificial neural network (ANN) has been trained to calculate the relative activities of 32 radioisotopes in a spectrum. The ANN is trained with simulated gamma-ray spectra, allowing easy expansion of the library of target radioisotopes. In this paper, we present our initial algorithms based on an ANN and evaluate them against a series of measured and simulated spectra.

[1]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[2]  S. Jeyanthi,et al.  Neural network based automatic fingerprint recognition system for overlapped latent images , 2015, J. Intell. Fuzzy Syst..

[3]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[4]  John S. Hendricks,et al.  Features of MCNP6 , 2014, ICS 2014.

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Liang Chen,et al.  Nuclide identification algorithm based on K–L transform and neural networks , 2009 .

[7]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[8]  Germà Garcia-Belmonte,et al.  A new approach to automatic radiation spectrum analysis , 1991 .

[9]  Alice Zheng,et al.  Evaluating Machine Learning Models , 2019, Machine Learning in the AWS Cloud.

[10]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[11]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[12]  Xiao-Hu Yu,et al.  Efficient Backpropagation Learning Using Optimal Learning Rate and Momentum , 1997, Neural Networks.

[13]  Kiyoshi Shizuma,et al.  Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer , 2002 .

[14]  M. Huver,et al.  Application of neural networks to quantitative spectrometry analysis , 1999 .

[15]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[16]  Aaron R. Rababaah,et al.  Integration of Two Different Signal Processing Techniques with Artificial Neural Network for Stock Market Forecasting , 2015 .