Development of an efficient technique for constructing energy spectrum of NaI(Tl) detector using spectrum of NE102 detector based on supervised model-free methods

摘要

The motivation of this study is development of a technique to construct energy spectrum of higher price/high resolution detectors (e.g. NaI (Tl)) using spectrum of lower price/low resolution detectors (e.g. NE102). Since there is no explicit mathematical model between these type of detectors (i.e. organic and inorganic scintillator detectors), it is necessary to utilize model-free methods. Construction of mapping function to generate spectrum of inorganic scintillator using spectrum of organic scintillator can be done by supervised model-free methods. Different supervised learning methods including localized neural networks, statistical methods, feed-forward neural networks, and conditional methods are utilized for spectrum construction. Experimental spectrums of the different radioisotopes (i.e. Co-60, Cs-137, Na-22, Am-241) including 15 spectrums of NaI (Tl) detector and 15 spectrums of NE102 detector are respectively used as training data and test data in the supervised methods. Results demonstrate that localized network (i.e. radial basis network) is the more appropriate method for the spectrum construction. The results of statistical method (i.e. support vector machine) is acceptable while conditional method (i.e. decision tree) does not give acceptable results and multi-layer perceptron does not learn the spectrums. The developed technique can be applied with an interesting ratio of training set to test set (i.e. r/(2(r)-1-r)). In other words, constructing spectrums of all possible combinations of r radioisotopes (i.e. 2(r)-1-r) is possible only with training of single radioisotopes spectrums (i.e. r). The developed method for generation of spectrums is more appropriate for identification of the radioisotopes and is not so useful for spectrum tracking. Spectrum tracking can be done by training of supervised learning method using generated pulses of detector.